On-site market-based unit auxiliary analysis system

Abstract

The application relates to the technical field of power spot transaction, and discloses a unit auxiliary analysis system based on a spot market, which comprises a user matrix generation module, a matrix updating module, a reference user set generation module and a power supply curve generation module. The user matrix generation module is used for generating a user matrix; the matrix updating module is used for updating a reference matrix based on the information of historical users; the reference user set generation module is used for selecting a second unit and extracting a reference user set generated by the historical users; the power supply curve generation module is used for generating a reference power load curve of a power purchasing user by compounding the average power load curves of all the historical users in the reference user set; and the unit auxiliary adjustment module is used for adjusting the main unit and the auxiliary unit of a power plant based on a heat supply output curve and the reference power load curve. The application can predict and analyze the power demand of a buyer in the spot market transaction through the reference of historical data, and can reasonably and timely adjust the operation state of the unit to match the output power load required by the power plant.

Description

Technical Field

[0001] This invention relates to the field of electricity spot trading technology, and more specifically, to a generator auxiliary analysis system based on the spot market. Background Technology

[0002] Because the production, distribution, and consumption of electricity are almost instantaneous and cannot be stored in large quantities, electricity supply and demand must maintain a real-time balance. To ensure power supply security, precise control by dispatching agencies is required. This necessitates not only a sound overall market regulation mechanism but also the ability of power plants (sellers) to adjust their operating status based on transaction conditions. However, it is impractical to adjust the operating status of generating units in real time according to the electricity demand of buyers. This leads to power plants being unable to rationally regulate unit operation and having to maintain a margin of safety, impacting unit efficiency. For thermal power plants, this problem is even more severe because their units need to supply both heat and electricity simultaneously. Summary of the Invention

[0003] This invention provides a unit auxiliary analysis system based on the spot market, which solves the technical problem in related technologies that power plants cannot reasonably adjust unit operation after spot market transactions.

[0004] According to one aspect of the present invention, a generator auxiliary analysis system based on the spot market is provided, comprising:

[0005] The transaction information filtering module is used to filter spot market transaction information that meets the conditions of power plants;

[0006] The transaction information processing module obtains electricity purchaser information based on the filtered spot market transaction information;

[0007] The attributes of electricity purchasers are generated based on their information. These attributes include: the company's electricity consumption in the previous year, the company's total revenue in the previous year, the company's maximum electricity load, the company's minimum electricity load, the company's average electricity load, the longitude of the company's location, and the latitude of the company's location.

[0008] Historical data storage module, which is used to store information of historical users;

[0009] The user matrix generation module is used to generate a user matrix with 16 elements, and then randomly select 16 historical users as elements of the user matrix.

[0010] The matrix update module updates the user matrix and generates a reference matrix based on historical user information.

[0011] The reference user set generation module is used to calculate the second similarity between electricity purchasers and elements of the reference matrix, and then select the elements of the reference matrix with the highest second similarity to electricity purchasers as the second unit. Historical users within the historical user set whose second similarity to the second unit is greater than the second similarity threshold are extracted to generate the reference user set.

[0012] The power supply curve generation module is used to extract the average electricity load curve of historical users in the reference user set; and to generate the reference electricity load curve of the purchasing user by combining the average electricity load curves of all historical users in the reference user set.

[0013] The unit auxiliary regulation module adjusts the main and auxiliary units of the power plant based on the heat output curve and the reference power load curve.

[0014] Furthermore, the criteria for filtering transaction information include unit price and purchased electricity volume.

[0015] Furthermore, the information of historical users includes the attributes of historical users, which also include the company's electricity consumption in the previous year, the company's total revenue in the previous year, the company's maximum electricity load, the company's minimum electricity load, the company's average electricity load, the longitude of the company's location, and the latitude of the company's location.

[0016] Furthermore, updating the user matrix to generate the reference matrix includes the following steps:

[0017] Step 1: Extract historical users to generate a historical user set;

[0018] Step 2: Randomly select a historical user from the historical user set, calculate the first similarity between the historical user and the elements of the user matrix, select the element with the highest first similarity to the historical user in the user matrix as the first element, and then update the first element and its neighboring elements.

[0019] The difference between the number of rows of the neighboring elements of the first element and the number of rows of the first element is 1, or the difference between the number of columns of the neighboring elements of the first element and the number of columns of the first element is 1.

[0020] The formula for calculating the first similarity is as follows:

[0021]

[0022] Where x 1i Let x be the i-th general property of the element in the square matrix. 2i Let be the i-th general attribute of the historical user, n be the total number of general attributes of the elements in the square matrix, and D2 be the second distance between the historical user and the elements of the user square matrix.

[0023]

[0024] Wherein, j1 and j2 are the regional attributes corresponding to the longitude of the enterprise location of the historical user and the element of the matrix, respectively, and w1 and w2 are the regional attributes corresponding to the latitude of the enterprise location of the historical user and the element of the matrix, respectively.

[0025] Step 3: Iterate through Step 2 until the number of times the elements of the user matrix are updated reaches the set threshold, then end the step and update the user matrix to obtain the reference matrix.

[0026] Furthermore, the formula for updating the elements of the user matrix is ​​as follows:

[0027]

[0028] in This represents the value of the k-th attribute of an element in the updated user matrix. This represents the value of the k-th attribute of the element in the user matrix before the update. represents the value of the k-th attribute of a historical user, and t represents the number of times the elements of the user matrix have been updated.

[0029] Furthermore, the formula for calculating the second similarity is as follows:

[0030]

[0031] Where x 3i To refer to the i-th attribute of an element in the square matrix, x 4i Let be the i-th general attribute of the electricity purchaser, and n be the total number of general attributes of the elements in the reference matrix.

[0032] Furthermore, the functional representation of the average electricity load curve is:

[0033] y1 = f1(t)

[0034] Where y1 represents the electricity load at the corresponding time, which is the average electricity load of the user at the same time point every day within a month, and t represents the time point of a day.

[0035] The average electricity load curves of all historical users in the reference user set are combined to generate the reference electricity load curve of the electricity purchasing user.

[0036] The functional representation of the reference electricity load curve is:

[0037] y2 = f2(t);

[0038] Where y2 represents the electricity load at the corresponding time, and t represents a time point in a day.

[0039] Furthermore, the composite method calculates the average electricity load of the average electricity load curve of all historical users at each time point, and uses the averaged electricity load as the electricity load value of the reference electricity load curve.

[0040] Furthermore, the adjustment strategies of the unit's auxiliary control module include:

[0041] The total heat consumption of a power plant when all units occupy 20% of their maximum heat consumption is called the first rated heat consumption.

[0042] All auxiliary units occupy 20% of their maximum heat consumption, and the main unit occupies 100% of its maximum heat consumption. The total heat consumption of the power plant is called the second rated heat consumption.

[0043] If the required heat consumption is lower than or equal to the first rated heat consumption, the main unit will maintain 20% of its maximum heat consumption, and the required heat consumption of each auxiliary unit will be allocated to each auxiliary unit in turn, up to 20% of its maximum heat consumption, until the remaining required heat consumption is less than 20% of the maximum heat consumption of an auxiliary unit. At this point, the allocation will stop, the unallocated required heat consumption will be allocated to the main unit, and the auxiliary units with unallocated required heat consumption will be shut down. At this time, all the heat consumption used for power generation will be allocated to the auxiliary units that have not been shut down.

[0044] If the required heat consumption exceeds the first rated heat consumption but is less than the second rated heat consumption, all units will be started. The required heat consumption of the auxiliary units will be equal to 20% of the maximum heat consumption of the auxiliary units. If the required heat consumption of the output electrical load is less than 50% of the maximum heat consumption of the main unit, the required heat consumption of the output electrical load will be allocated to the main unit.

[0045] If the required heat consumption exceeds the second rated heat consumption, all units are started. The required heat consumption of the main unit reaches 100% of its maximum heat consumption. For the auxiliary units, 20% of the maximum heat consumption is allocated first, and then the remaining required heat consumption is allocated to the auxiliary units one by one. When allocating one by one, the required heat consumption of one auxiliary unit is accumulated to its maximum heat consumption before the next auxiliary unit is allocated.

[0046] If the heat consumption demanded by the output electrical load is greater than or equal to 50% of the maximum heat consumption of the main unit, the heat consumption demanded by the output electrical load will be preferentially allocated to the main unit until the heat consumption demanded by the output electrical load allocated to the main unit reaches 80% of the maximum heat consumption of the main unit. Then the remaining heat consumption demanded by the output electrical load will be allocated to the auxiliary units in sequence until the heat consumption demanded by the output electrical load allocated to the auxiliary units reaches 20% of the maximum heat consumption of the auxiliary units.

[0047] According to one aspect of the present invention, a generator set auxiliary analysis method based on the spot market is provided, which performs the following steps through the aforementioned generator set auxiliary analysis system based on the spot market:

[0048] Step 11: Filter spot market transaction information that meets the power plant criteria;

[0049] Step 12: Obtain electricity purchaser information based on the spot market transaction information obtained through screening;

[0050] Step 13: Generate a user matrix with 16 elements, and then randomly select 16 historical users as elements of the user matrix;

[0051] Step 14: Update the user matrix and generate a reference matrix based on historical user information;

[0052] Step 15: Calculate the second similarity between the electricity purchaser and the elements of the reference matrix, then select the element of the reference matrix with the largest second similarity to the electricity purchaser as the second unit, and extract historical users in the historical user set whose second similarity to the second unit is greater than the second similarity threshold to generate the reference user set.

[0053] Step 16: Generate the reference electricity load curve for the electricity purchaser by combining the average electricity load curves of all historical users in the reference user set.

[0054] Step 17: Adjust the main and auxiliary units of the power plant based on the heat output curve and the reference electricity load curve.

[0055] The beneficial effects of this invention are as follows:

[0056] This invention uses historical data to predict and analyze the electricity demand of buyers in the spot market, enabling reasonable and timely adjustment of the unit's operating status to match the power plant's required output load. It is particularly suitable for thermal power plants and similar combined heat and power plants. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the unit auxiliary analysis system based on the spot market of the present invention;

[0058] Figure 2 This is a schematic diagram of the matrix update module of the present invention;

[0059] Figure 3 This is a schematic diagram of the power supply curve generation module of the present invention;

[0060] Figure 4 This is a flowchart of the process for generating the reference matrix for updating the user matrix according to the present invention.

[0061] Figure 5This is a flowchart of the unit auxiliary analysis method based on the spot market of the present invention.

[0062] In the diagram: Transaction information filtering module 101, transaction information processing module 102, historical data storage module 103, user matrix generation module 104, matrix update module 105, historical user set generation module 1051, matrix update unit 1052, iterative execution module 1053, reference user set generation module 106, power supply curve generation module 107, average power load curve extraction module 1071, reference power load curve generation module 1072, and unit auxiliary regulation module 108. Detailed Implementation

[0063] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0064] Example 1

[0065] like Figures 1-3 As shown, the unit auxiliary analysis system based on the spot market includes:

[0066] The transaction information filtering module 101 is used to filter transaction information in the spot market that meets the conditions of the power plant;

[0067] In one embodiment of the present invention, the filtering conditions include unit price and purchased electricity volume; for example, transaction information with a unit price lower than a set threshold is excluded, or transaction information with a purchased electricity volume higher than the power plant's maximum power generation is excluded. If the purchased electricity volume is in daily units, then the comparison is made with the corresponding power plant's maximum power generation on that day.

[0068] Transaction information processing module 102 obtains electricity purchaser information based on the filtered spot market transaction information;

[0069] The attributes of electricity purchasers are generated based on their information. These attributes include:

[0070] The company's electricity consumption in the previous year, total revenue in the previous year, maximum electricity load, minimum electricity load, average electricity load, longitude of the company's location, and latitude of the company's location;

[0071] Historical data storage module 103 is used to store information of historical users;

[0072] Historical user information includes the attributes of historical users, which also include the company's electricity consumption in the previous year, the company's total revenue in the previous year, the company's maximum electricity load, the company's minimum electricity load, the company's average electricity load, the longitude of the company's location, and the latitude of the company's location.

[0073] User matrix generation module 104 is used to generate a user matrix with 16 elements, and then randomly select 16 historical users as elements of the user matrix.

[0074] The matrix update module 105 updates the user matrix and generates a reference matrix based on the information of historical users.

[0075] like Figure 4 As shown, the update steps include:

[0076] Step 1: Extract historical users to generate a historical user set;

[0077] Step 2: Randomly select a historical user from the historical user set, calculate the first similarity between the historical user and the elements of the user matrix, select the element with the highest first similarity to the historical user in the user matrix as the first element, and then update the first element and its neighboring elements.

[0078] The difference between the number of rows of the neighboring elements of the first element and the number of rows of the first element is 1, or the difference between the number of columns of the neighboring elements of the first element and the number of columns of the first element is 1.

[0079] The formula for calculating the first similarity is as follows:

[0080]

[0081] Where x 1i Let x be the i-th general property of the element in the square matrix. 2i Let be the i-th general attribute of the historical user, n be the total number of general attributes of the elements in the square matrix, and D2 be the second distance between the historical user and the elements of the user square matrix.

[0082]

[0083] Wherein, j1 and j2 are the regional attributes corresponding to the longitude of the enterprise location of the historical user and the element of the matrix, respectively, and w1 and w2 are the regional attributes corresponding to the latitude of the enterprise location of the historical user and the element of the matrix, respectively.

[0084] General attributes are those other than regional attributes.

[0085] Step 3: Iterate through Step 2 until the number of times the elements of the user matrix are updated reaches the set threshold, then end the step and update the user matrix to obtain the reference matrix.

[0086] In one embodiment of the present invention, the number of times threshold is N. 2 .

[0087] The formula for updating the elements of the user matrix is ​​as follows:

[0088]

[0089] in This represents the value of the k-th attribute of an element in the updated user matrix. This represents the value of the k-th attribute of the element in the user matrix before the update. The value of the k-th attribute of a historical user is represented by t, and t represents the number of times the elements of the user matrix have been updated (updating an element of a user matrix is ​​counted as updating the element of the user matrix once).

[0090] Matrix update module 105 includes:

[0091] The historical user set generation module 1051 is used to randomly extract a set number of historical users to generate a historical user set.

[0092] The matrix update unit 1052 is used to randomly select a historical user from the historical user set, calculate the first similarity between the historical user and the elements of the user matrix, select the element with the largest first similarity with the historical user in the user matrix as the first element, and then update the first element and the neighboring elements of the first element.

[0093] The iterative execution module 1053 is used to control the number of times the matrix update unit is started, until the number of times the elements of the user matrix are updated reaches the set threshold, and then the step ends, updating the user matrix to obtain the reference matrix.

[0094] The reference user set generation module 106 is used to calculate the second similarity between the electricity purchaser and the elements of the reference matrix, and then select the element of the reference matrix with the largest second similarity with the electricity purchaser as the second unit, and extract historical users in the historical user set whose second similarity with the second unit is greater than the second similarity threshold to generate the reference user set.

[0095] The formula for calculating the second similarity is as follows:

[0096]

[0097] Where x 3i To refer to the i-th attribute of an element in the square matrix, x 4i Let be the i-th general attribute of the electricity purchaser, and n be the total number of general attributes of the elements in the reference matrix.

[0098] The power supply curve generation module 107 is used to extract the average power load curve of historical users in the reference user set.

[0099] The functional representation of the average electricity load curve is:

[0100] y1 = f1(t)

[0101] Where y1 represents the electricity load at the corresponding time, which is the average electricity load of the user at the same time point every day within a month, and t represents the time point of a day.

[0102] The average electricity load curves of all historical users in the reference user set are combined to generate the reference electricity load curve of the electricity purchasing user.

[0103] The functional representation of the reference electricity load curve is:

[0104] y2=f2(t)

[0105] Where y2 represents the electricity load at the corresponding time, and t represents a time point in a day.

[0106] The composite method calculates the average electricity load of all historical users at each time point, and uses the averaged electricity load as the reference electricity load value.

[0107] The beneficial effects of the above embodiments are as follows: by mining heterogeneous data from multiple dimensions such as region, more extensive data mining is carried out on the basis of data from users of this plant and users of other power plants. Through detailed data of historical users, reliable estimation of the electricity load curve of electricity purchasers in the spot trading market is made, which serves as the basis for unit regulation and control. This improves the accuracy of thermal power plants in predicting the heating and power supply needs of their units, thereby improving the overall economic benefits of thermal power plants.

[0108] In one embodiment of the present invention, the power supply curve generation module 107 includes:

[0109] The average electricity load curve extraction module 1071 is used to extract the average electricity load curve of historical users in the reference user set.

[0110] The reference electricity load curve generation module 1072 is used to generate the reference electricity load curve for the electricity purchaser by combining the average electricity load curves of all historical users in the reference user set.

[0111] Since the changes in the heating users of the power plant are relatively small, the changes in the heating output of the electric field are also relatively small in the short period of time. That is, the daily heating output curve of the power plant changes relatively small. The historical heating output curve can be used as the heating output curve for the current day, or the heating output curve for the current day can be estimated from the historical heating output curve.

[0112] The unit auxiliary regulation module 108 adjusts the main and auxiliary units of the power plant based on the heat output curve and the reference power load curve;

[0113] In one embodiment of the present invention, the required heat consumption is calculated based on the output heating and output electrical loads. The calculation formula for the required heat consumption is as follows:

[0114]

[0115] Among them, X q Indicates the required heat consumption. This indicates the amount of heat consumed to meet the heating demand. This indicates the heat consumption required by the output electrical load.

[0116] The total heat consumption of a power plant when all units occupy 20% of their maximum heat consumption is called the first rated heat consumption.

[0117] All auxiliary units occupy 20% of their maximum heat consumption, and the main unit occupies 100% of its maximum heat consumption. The total heat consumption of the power plant is called the second rated heat consumption.

[0118] If the required heat consumption is lower than or equal to the first rated heat consumption, the main unit will maintain 20% of its maximum heat consumption (for a normally operating power plant, the required heat consumption will generally not be lower than 20% of the main unit's maximum heat consumption). The required heat consumption of 20% of the maximum heat consumption of each auxiliary unit will be allocated one by one until the remaining required heat consumption is less than 20% of the maximum heat consumption of an auxiliary unit. At this point, the allocation will stop, and the unallocated required heat consumption will be allocated to the main unit. Then, the auxiliary units with unallocated required heat consumption will be shut down. At this time, all the heat consumption used for power generation will be allocated to the auxiliary units that have not been shut down (under low heat consumption conditions, the output load is low and can be met by the auxiliary units, and generally the total maximum heat consumption of the power plant's auxiliary units will not exceed that of the main unit).

[0119] If the required heat consumption exceeds the first rated heat consumption but is less than the second rated heat consumption, all units will be started. The required heat consumption of the auxiliary units will be equal to 20% of the maximum heat consumption of the auxiliary units. If the required heat consumption of the output electrical load is less than 50% of the maximum heat consumption of the main unit, the required heat consumption of the output electrical load will be allocated to the main unit.

[0120] If the required heat consumption exceeds the second rated heat consumption, all units are started. The required heat consumption of the main unit reaches 100% of its maximum heat consumption. For the auxiliary units, 20% of the maximum heat consumption is allocated first, and then the remaining required heat consumption is allocated to the auxiliary units one by one. When allocating one by one, the required heat consumption of one auxiliary unit is accumulated to its maximum heat consumption before the next auxiliary unit is allocated.

[0121] If the heat consumption demanded by the output electrical load is greater than or equal to 50% of the maximum heat consumption of the main unit, the heat consumption demanded by the output electrical load will be preferentially allocated to the main unit until the heat consumption demanded by the output electrical load allocated to the main unit reaches 80% of the maximum heat consumption of the main unit. Then the remaining heat consumption demanded by the output electrical load will be allocated to the auxiliary units in sequence until the heat consumption demanded by the output electrical load allocated to the auxiliary units reaches 20% of the maximum heat consumption of the auxiliary units.

[0122] Considering the characteristics of the generating units used in combined heat and power (CHP) systems of thermal power plants, we aim to maximize the efficiency of these units and improve the fuel utilization coefficient of the thermal power plant.

[0123] The beneficial effects of the above embodiments are as follows: by generating an electrical load curve based on historical data, the main and auxiliary units of the thermal power plant can be adjusted, thereby improving the economic efficiency of the thermal power plant operation; when making adjustments, adjustment strategies are used to allocate the heat consumption of the main and auxiliary units according to different rated heat consumption, thereby improving the operating efficiency of the main and auxiliary units of the thermal power plant.

[0124] like Figure 5 As shown, this embodiment provides a generator set auxiliary analysis method based on the spot market, which performs the following steps through the aforementioned generator set auxiliary analysis system based on the spot market:

[0125] Step 11: Filter spot market transaction information that meets the power plant criteria;

[0126] Step 12: Obtain electricity purchaser information based on the spot market transaction information obtained through screening;

[0127] Step 13: Generate a user matrix with 16 elements, and then randomly select 16 historical users as elements of the user matrix;

[0128] Step 14: Update the user matrix and generate a reference matrix based on historical user information;

[0129] Step 15: Calculate the second similarity between the electricity purchaser and the elements of the reference matrix, then select the element of the reference matrix with the largest second similarity to the electricity purchaser as the second unit, and extract historical users in the historical user set whose second similarity to the second unit is greater than the second similarity threshold to generate the reference user set.

[0130] Step 16: Generate the reference electricity load curve for the electricity purchaser by combining the average electricity load curves of all historical users in the reference user set.

[0131] Step 17: Adjust the main and auxiliary units of the power plant based on the heat output curve and the reference electricity load curve.

[0132] The beneficial effects of the above embodiments are as follows: by analyzing the transaction information of the spot market under the conditions of the load power plant and generating the user's electricity load curve, the thermal power plant can more accurately adjust the main unit and auxiliary unit according to the electricity load curve, thereby reducing energy waste and improving the economic efficiency of the thermal power plant.

[0133] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A generator unit auxiliary analysis system based on the spot market, characterized in that, include: The transaction information filtering module is used to filter spot market transaction information that meets the conditions of power plants; The transaction information processing module obtains electricity purchaser information based on the filtered spot market transaction information; The attributes of electricity purchasers are generated based on their information. These attributes include: the company's electricity consumption in the previous year, the company's total revenue in the previous year, the company's maximum electricity load, the company's minimum electricity load, the company's average electricity load, the longitude of the company's location, and the latitude of the company's location. Historical data storage module, which is used to store information of historical users; The user matrix generation module is used to generate a user matrix with 16 elements, and then randomly select 16 historical users as elements of the user matrix. The matrix update module updates the user matrix and generates a reference matrix based on historical user information. The reference user set generation module is used to calculate the second similarity between electricity purchasers and elements of the reference matrix, and then select the elements of the reference matrix with the highest second similarity to electricity purchasers as the second unit. Historical users within the historical user set whose second similarity to the second unit is greater than the second similarity threshold are extracted to generate the reference user set. The power supply curve generation module is used to extract the average electricity load curve of historical users in the reference user set; and to generate the reference electricity load curve of the purchasing user by combining the average electricity load curves of all historical users in the reference user set. The unit auxiliary regulation module adjusts the main and auxiliary units of the power plant based on the heat output curve and the reference power load curve.

2. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, The criteria for filtering transaction information include unit price and purchased electricity volume.

3. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, Historical user information includes the attributes of historical users, which also include the company's electricity consumption in the previous year, the company's total revenue in the previous year, the company's maximum electricity load, the company's minimum electricity load, the company's average electricity load, the longitude of the company's location, and the latitude of the company's location.

4. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, Updating the user matrix to generate the reference matrix includes the following steps: Step 1: Extract historical users to generate a historical user set; Step 2: Randomly select a historical user from the historical user set, calculate the first similarity between the historical user and the elements of the user matrix, select the element with the highest first similarity to the historical user in the user matrix as the first element, and then update the first element and its neighboring elements. The difference between the number of rows of the neighboring elements of the first element and the number of rows of the first element is 1, or the difference between the number of columns of the neighboring elements of the first element and the number of columns of the first element is 1. The formula for calculating the first similarity is as follows: in Let i be the i-th general property of the element in the square matrix. Let be the i-th general attribute of a historical user, and n be the total number of general attributes of the elements in the matrix. The second distance between historical users and elements of the user matrix; Step 3: Iterate through Step 2 until the number of times the elements of the user matrix are updated reaches the set threshold, then end the step and update the user matrix to obtain the reference matrix.

5. The unit auxiliary analysis system based on the spot market according to claim 4, characterized in that, The formula for updating the elements of the user matrix is ​​as follows: in This represents the value of the k-th attribute of an element in the updated user matrix. This represents the value of the k-th attribute of the element in the user matrix before the update. represents the value of the k-th attribute of a historical user, and t represents the number of times the elements of the user matrix have been updated.

6. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, The formula for calculating the second similarity is as follows: in For the i-th attribute of the reference matrix element, Let be the i-th general attribute of the electricity purchaser, and n be the total number of general attributes of the elements in the reference matrix.

7. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, The functional representation of the average electricity load curve is: in The electricity load at a given time is the average electricity load of the user at the same time each day within a month, where t represents a day. The average electricity load curves of all historical users in the reference user set are combined to generate the reference electricity load curve of the electricity purchasing user. The functional representation of the reference electricity load curve is: ； in This represents the electricity load at a given time, where t represents a point in time within a day.

8. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, The composite method calculates the average electricity load of all historical users at each time point, and uses the averaged electricity load as the reference electricity load value.

9. The unit auxiliary analysis system based on the spot market according to claim 1, characterized in that, The adjustment strategies of the unit's auxiliary control module include: The required heat consumption is calculated based on the output heating and output electrical loads. The formula for calculating the required heat consumption is as follows: in, Indicates the required heat consumption. This indicates the amount of heat consumed to meet the heating demand. This indicates the heat consumption required by the output electrical load. The total heat consumption of a power plant when all units occupy 20% of their maximum heat consumption is called the first rated heat consumption. All auxiliary units occupy 20% of their maximum heat consumption, and the main unit occupies 100% of its maximum heat consumption. The total heat consumption of the power plant is called the second rated heat consumption. If the required heat consumption is lower than or equal to the first rated heat consumption, the main unit will occupy 20% of its own maximum heat consumption, and the required heat consumption of each auxiliary unit will be allocated to each auxiliary unit in turn, up to 20% of its own maximum heat consumption, until the remaining required heat consumption is less than 20% of the maximum heat consumption of an auxiliary unit. At this time, the allocation will stop, the unallocated required heat consumption will be allocated to the main unit, and the auxiliary units with unallocated required heat consumption will be shut down. At this time, all the heat consumption used for power generation will be allocated to the auxiliary units that have not been shut down. If the required heat consumption exceeds the first rated heat consumption but is less than the second rated heat consumption, all units will be started. The required heat consumption of the auxiliary units will be equal to 20% of the maximum heat consumption of the auxiliary units. If the required heat consumption of the output electrical load is less than 50% of the maximum heat consumption of the main unit, the required heat consumption of the output electrical load will be allocated to the main unit. If the required heat consumption exceeds the second rated heat consumption, all units will be started. The required heat consumption of the main unit will reach 100% of its maximum heat consumption. For the auxiliary units, 20% of the maximum heat consumption will be allocated first, and then the remaining required heat consumption will be allocated to the auxiliary units one by one. When allocating one by one, the required heat consumption of one auxiliary unit will be accumulated to its maximum heat consumption before the next auxiliary unit will be allocated. If the heat consumption demanded by the output electrical load is greater than or equal to 50% of the maximum heat consumption of the main unit, the heat consumption demanded by the output electrical load will be preferentially allocated to the main unit until the heat consumption demanded by the output electrical load allocated to the main unit reaches 80% of the maximum heat consumption of the main unit. Then the remaining heat consumption demanded by the output electrical load will be allocated to the auxiliary units. The allocation will continue in sequence until the heat consumption demanded by the output electrical load allocated to the auxiliary units reaches 20% of the maximum heat consumption of the auxiliary units.

10. A unit auxiliary analysis method based on the spot market, characterized in that, It performs the following steps using the spot market-based unit auxiliary analysis system as described in any one of claims 1-9: Step 11: Filter spot market transaction information that meets the power plant criteria; Step 12: Obtain electricity purchaser information based on the spot market transaction information obtained through screening; Step 13: Generate a user matrix with 16 elements, and then randomly select 16 historical users as elements of the user matrix; Step 14: Update the user matrix and generate a reference matrix based on historical user information; Step 15: Calculate the second similarity between the electricity purchaser and the elements of the reference matrix, then select the element of the reference matrix with the largest second similarity to the electricity purchaser as the second unit, and extract historical users in the historical user set whose second similarity to the second unit is greater than the second similarity threshold to generate the reference user set. Step 16: Generate the reference electricity load curve for the electricity purchaser by combining the average electricity load curves of all historical users in the reference user set. Step 17: Adjust the main and auxiliary units of the power plant based on the heat output curve and the reference electricity load curve.

Images