Power system emission factor prediction method and device based on time-varying power transmission constraint

Abstract

The invention provides a power system emission factor prediction method and device based on time-varying power transmission constraint, and can be applied to the technical field of power carbon emission and the technical field of artificial intelligence. The power system emission factor prediction method based on the time-varying power transmission constraint comprises the following steps: acquiring power transmission planning information of a region from a database by using a data interface; updating a current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix, the current power transmission matrix comprising a plurality of elements, the elements being current power transmission data among the plurality of sub-regions, and the current power transmission data being obtained through a sensor deployed in the power system; according to the target power transmission matrix and the power constraint condition, predicting the respective power generation installed capacity of the plurality of sub-regions to obtain the respective target power generation installed capacity of the plurality of sub-regions; and according to the target power generation installed capacity, predicting respective emission factor data of the plurality of sub-regions.

Description

Technical Field

[0001] The present disclosure relates to the fields of power carbon emission technology and artificial intelligence technology, and more specifically to a method and device for predicting power system emission factors based on time-varying power transmission constraints.

[0002] Background technology In related technologies, electronic devices such as servers and mobile terminals can be used to process data related to the emission factors of power systems in the field of big data. Currently, an emission factor prediction system can be set up in electronic devices such as servers or mobile terminals, and the emission factor prediction system processes historical factor data that affects the emission factors of the power system to predict the emission factor data of the region. However, due to the large amount of historical factor data in the power system, when using this data to predict the emission factor, not only will the execution efficiency of electronic devices such as servers and mobile terminals be low and resource consumption be high; but there will also be a lot of data noise, which will lead to a large difference between the predicted emission factor results and the actual emission factor, and the prediction effect will be poor. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides a method and apparatus for predicting power system emission factors based on time-varying power transmission constraints.

[0004] According to a first aspect of the present disclosure, a method for predicting emission factors of a power system based on time-varying power transmission constraints is provided, which is applied to a service end and includes: obtaining power transmission planning information of a region from a database using a data interface, where the region includes multiple sub-regions with power systems, and the power transmission planning information represents the power transmission situation between the multiple sub-regions in the future; updating the current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix, where the current power transmission matrix includes multiple elements, where the elements are current power transmission data between the multiple sub-regions, and the current power transmission data is obtained through sensors deployed in the power system; predicting the respective installed power generation capacity of the multiple sub-regions according to the target power transmission matrix and power constraints to obtain the respective target installed power generation capacity of the multiple sub-regions, where the installed power generation capacity represents the power supply performance of the power system of the sub-region; and predicting the emission factor data of the multiple sub-regions according to the target installed power generation capacity.

[0005] According to an embodiment of the present disclosure, the installed power generation capacity of each of the multiple sub-regions is predicted according to the target power transmission matrix and the power constraint conditions to obtain the target installed power generation capacity of each of the multiple sub-regions, including: predicting the installed power generation capacity of the multiple sub-regions according to the target power transmission matrix and the power constraint conditions to obtain a candidate prediction set of the region, the candidate prediction set including the candidate prediction subsets of each of the multiple sub-regions, the candidate prediction subset including at least one candidate installed power generation capacity; determining the target prediction subset from the candidate prediction set according to the installed power generation capacity condition of the region, the target prediction subset including determining the target installed power generation capacity from at least one candidate installed power generation capacity.

[0006] According to an embodiment of the present disclosure, the installed power generation capacity condition of a region is that the sum of different types of installed power generation capacities in the region reaches a target threshold.

[0007] According to an embodiment of the present disclosure, obtaining power transmission planning information of a region includes: obtaining initial power information of the region about future time from a database based on the current time and sub-region identifier; and filtering the initial power information according to preset keywords to obtain power transmission planning information.

[0008] According to an embodiment of the present disclosure, a current power transmission matrix is ​​updated based on power transmission planning information to obtain a target power transmission matrix, including: performing semantic analysis on the power transmission planning information according to the sub-area identifier to obtain an update type for the sub-area; inputting current power transmission data into a prediction model corresponding to the update type to obtain target power transmission data; and updating the current power transmission matrix using the target power transmission data to obtain the target power transmission matrix.

[0009] According to an embodiment of the present disclosure, the power constraint conditions include: a balance constraint condition between power consumption and power production of a region, a transmission constraint condition between multiple sub-regions, and a preset constraint condition of power generation type.

[0010] According to an embodiment of the present disclosure, the balance constraint condition represents that the power consumption of a region is equal to the power production.

[0011] According to an embodiment of the present disclosure, the transmission constraint condition characterizes that the sum of the power consumption and power output of a sub-region is equal to the sum of the power production and power input of the sub-region, the power output is the amount of power transmitted from a sub-region to the remaining sub-regions in the region, and the power input is the amount of power transmitted from the remaining sub-regions to the sub-region.

[0012] According to an embodiment of the present disclosure, the preset constraint condition of the power generation type includes: the type power generation installed capacity of the power generation type meets the preset threshold condition.

[0013] A second aspect of the present disclosure provides a power system emission factor prediction device based on time-varying power transmission constraints, characterized in that the device includes: an acquisition module for acquiring power transmission planning information of a region, the region including multiple sub-regions, and the power transmission planning information characterizing the power transmission situation between the multiple sub-regions in the future; an update module for updating the current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix, and the current power transmission matrix is ​​established based on the current power transmission data between the multiple sub-regions; a prediction module for predicting the installed power generation capacity of the multiple sub-regions based on the target power transmission matrix and power constraints to obtain a candidate prediction set of the region, the candidate prediction set including candidate prediction subsets of the multiple sub-regions, and the candidate prediction subset including at least one candidate installed power generation capacity; a determination module for determining the target prediction subset from the candidate prediction set of the region based on the installed power generation capacity conditions of the region, the target prediction subset including the target installed power generation capacity of the multiple sub-regions.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0017] According to an embodiment of the present disclosure, by utilizing a data interface to obtain regional power transmission planning information from a database, the power transmission planning information characterizes the power transmission situation between multiple sub-regions in the future. Therefore, the amount of data relative to the historical factor data of the power system emission factor is reduced and the noise data is less, which helps to improve the execution efficiency of electronic equipment and reduce resource consumption.

[0018] In addition, the current power transmission matrix is ​​updated based on power transmission planning information to obtain a target power transmission matrix. Based on the target power transmission matrix and power constraints, the installed power generation capacity of each of the multiple sub-regions is predicted to obtain a target installed power generation capacity that meets the actual constraints of the sub-regions. Based on the target installed power generation capacity, emission factor data for each of the multiple sub-regions is predicted. Because the target power transmission matrix better reflects the power transmission conditions between the multiple sub-regions in the future, data quality is further improved, enabling electronic equipment to improve data processing precision and prediction accuracy within limited computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0020] Figure 1 A diagram schematically illustrates an application scenario of a method for predicting power system emission factors based on time-varying power transmission constraints according to an embodiment of the present disclosure;

[0021] Figure 2 A flowchart of a method for predicting power system emission factors based on time-varying power transmission constraints according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 3 A schematic diagram schematically illustrates a target installed power generation capacity of a sub-region at a future time according to an embodiment of the present disclosure;

[0023] Figure 4 A schematic diagram schematically illustrates target power output and target power input of a sub-region at a future time according to an embodiment of the present disclosure;

[0024] Figure 5 A schematic diagram schematically illustrates carbon emissions from power generation in a sub-region at a future time according to an embodiment of the present disclosure;

[0025] Figure 6 A schematic diagram schematically illustrates carbon emission factor data of a sub-region at a future time according to an embodiment of the present disclosure;

[0026] Figure 7 Schematically shows a structural block diagram of a power system emission factor prediction device based on time-varying power transmission constraints according to an embodiment of the present disclosure; and

[0027] Figure 8 The figure schematically shows a block diagram of an electronic device suitable for implementing a method for predicting emission factors of a power system based on time-varying power transmission constraints according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] In related technologies, electronic devices such as servers and mobile terminals can be used to process data related to the impact of power system emission factors in the big data field. Currently, an emission factor prediction system can be set up in electronic devices such as servers or mobile terminals. The emission factor prediction system processes historical factor data that affects the power system's electricity emission factors and predicts regional emission factor data. However, due to the large amount of historical factor data in the power system, using this data to predict emission factors will not only lead to low execution efficiency and high resource consumption of electronic devices such as servers and mobile terminals, but also cause a lot of data noise, resulting in a large difference between the predicted emission factor results and the actual emission factor, resulting in poor prediction results.

[0033] In view of this, an embodiment of the present disclosure provides a method for predicting emission factors of a power system based on time-varying power transmission constraints, including: using a data interface to obtain power transmission planning information of a region from a database, the region including multiple sub-regions with power systems, and the power transmission planning information characterizing the power transmission situation between the multiple sub-regions in the future; updating the current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix, the current power transmission matrix including multiple elements, the elements being current power transmission data between the multiple sub-regions, and the current power transmission data being obtained through sensors deployed in the power system; predicting the respective installed power generation capacity of the multiple sub-regions according to the target power transmission matrix and power constraints to obtain the respective target installed power generation capacity of the multiple sub-regions, the installed power generation capacity characterizing the power supply performance of the power system of the sub-region; and predicting the emission factor data of the multiple sub-regions according to the target installed power generation capacity.

[0034] Figure 1 The application scenario diagram of the power system emission factor prediction method based on time-varying power transmission constraints according to an embodiment of the present disclosure is schematically shown.

[0035] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0036] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0037] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0038] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0039] It should be noted that the method for predicting the emission factors of a power system based on time-varying power transmission constraints provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the device for predicting the emission factors of a power system based on time-varying power transmission constraints provided in the embodiment of the present disclosure can generally be set in the server 105. The method for predicting the emission factors of a power system based on time-varying power transmission constraints provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the device for predicting the emission factors of a power system based on time-varying power transmission constraints provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0040] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0041] Figure 2 The flowchart of the method for predicting power system emission factors based on time-varying power transmission constraints according to an embodiment of the present disclosure is schematically shown.

[0042] like Figure 2 As shown, the method for predicting power system emission factors based on time-varying power transmission constraints in this embodiment includes operations S210 to S240, and the method can be executed by a server.

[0043] In operation S210 , regional power transmission planning information is acquired from a database using a data interface.

[0044] According to an embodiment of the present disclosure, a region includes multiple sub-regions with power systems. For example, a region includes sub-region A, sub-region B, and sub-region C. The power generated by the power system in sub-region A can be transmitted to sub-regions B and C. The power generated by the power system in sub-region B can be transmitted only to sub-region C.

[0045] According to an embodiment of the present disclosure, the server is connected to the database via a data interface and can obtain regional power transmission planning information from the database. The power transmission planning information in the database can be extracted from information such as network and regional planning files.

[0046] The power transmission planning information, current power transmission matrix, etc. can be stored in the server's memory to facilitate subsequent prediction calls and improve processing efficiency.

[0047] The processor on the server side can execute the power system emission factor prediction method based on time-varying power transmission constraints.

[0048] According to an embodiment of the present disclosure, power transmission planning information represents the power transmission conditions between multiple sub-regions in the future. For example, the power transmission planning information may include a 5% reduction in the amount of electricity generated by renewable energy within a region over the next five years. Sub-region A within the region is planned to no longer utilize the power system for electricity generation, thereby reducing carbon emissions in sub-region A.

[0049] In operation S220 , the current power transmission matrix is ​​updated based on the power transmission planning information to obtain a target power transmission matrix.

[0050] According to an embodiment of the present disclosure, the current power transmission matrix includes multiple elements, where the elements are current power transmission data between multiple sub-areas, and the current power transmission data is obtained through sensors deployed in the power system.

[0051] For example, a region includes sub-region A, sub-region B, and sub-region C. The current power transmission matrix may be a 3×3 matrix, and may include current power transmission data transmitted from sub-region A to sub-region B, current power transmission data transmitted from sub-region B to sub-region A, current power transmission data transmitted from sub-region A to sub-region C, current power transmission data transmitted from sub-region C to sub-region A, current power transmission data transmitted from sub-region B to sub-region C, and current power transmission data transmitted from sub-region C to sub-region B. Other elements in the current power transmission matrix may be filled with 0s.

[0052] For example, sensors can be installed on the power systems of multiple sub-regions to obtain current power transmission data between the sub-regions. The sensors can be power sensors, electricity metering devices, current sensors, voltage sensors, etc.

[0053] According to an embodiment of the present disclosure, the target power transmission matrix may include a plurality of target elements, where the target elements are target power transmission data between a plurality of sub-areas at a future time.

[0054] In operation S230 , the power generation installed capacity of each of the plurality of sub-regions is predicted according to the target power transmission matrix and the power constraint condition to obtain the target power generation installed capacity of each of the plurality of sub-regions.

[0055] According to an embodiment of the present disclosure, the installed power generation capacity represents the power supply performance of the power system of the sub-region. For example, the installed power generation capacity is the total capacity of the generator sets in the power system, representing the maximum power supply capability of the power generation system.

[0056] According to an embodiment of the present disclosure, the power constraint condition may be determined based on equipment indicators of the power systems of the plurality of sub-regions, geographical attributes of the sub-regions, regional planning information, and the like.

[0057] According to the embodiment of the present disclosure, since the data volume of the power constraint condition is small, it can be stored in the memory of the server, which is convenient for calling the power constraint condition to predict the target power generation installed capacity of the sub-region in the future.

[0058] For example, if the equipment indicator of the power system in sub-region A is a preset value for the monthly power generation, the total amount of power transmitted from sub-region A to other sub-regions does not exceed the preset value.

[0059] For example, sub-region A is a coal-rich area with high electricity production. However, the regional planning information limits the amount of coal used in sub-region A. Therefore, the amount of coal used to generate electricity in sub-region A cannot exceed the limit.

[0060] According to an embodiment of the present disclosure, since the target power transmission matrix can characterize the power transmission situation between multiple sub-regions in the future, under the constraints of the target power transmission matrix and power constraints, the target power generation device capacity of the power system of the sub-region can be estimated.

[0061] In operation S240 , emission factor data of each of the plurality of sub-regions is predicted based on the target installed power generation capacity.

[0062] According to embodiments of the present disclosure, emission factors can be determined based on the non-renewable energy resources and the power generation capacity of the power system in a sub-region. For example, a power system that uses coal for power generation would have a higher emission factor, while a power system that uses renewable energy would have a lower emission factor.

[0063] For example, based on the target installed power generation capacity, the installed power generation capacity of non-renewable energy sources can be determined, and emission factor data for each of the multiple sub-regions can be predicted based on the installed power generation capacity of non-renewable energy sources.

[0064] According to an embodiment of the present disclosure, emission factor data of each of the plurality of sub-regions may be predicted based on a correlation formula between installed power generation capacity and emission factors.

[0065] According to an embodiment of the present disclosure, a first processing unit and a second processing unit in a processor of the server may be selected to respectively perform operation S230 and operation S240 .

[0066] The target power transmission matrix and power constraint conditions stored in the memory are input into the first processing unit, and the target power generation installed capacity of each of the multiple sub-regions can be predicted.

[0067] The target installed power generation capacity is input into the second processing unit to predict emission factor data for multiple sub-regions. This enables unit-by-unit prediction, reduces overlap in input and output data, and improves data processing efficiency.

[0068] According to an embodiment of the present disclosure, by utilizing a data interface to obtain regional power transmission planning information from a database, the power transmission planning information characterizes the power transmission situation between multiple sub-regions in the future. Therefore, the amount of data relative to the historical factor data of the power system emission factor is reduced and the noise data is less, which helps to improve the execution efficiency of electronic equipment and reduce resource consumption.

[0069] In addition, the current power transmission matrix is ​​updated based on power transmission planning information to obtain a target power transmission matrix. Based on the target power transmission matrix and power constraints, the installed power generation capacity of each of the multiple sub-regions is predicted to obtain a target installed power generation capacity that meets the actual constraints of the sub-regions. Based on the target installed power generation capacity, emission factor data for each of the multiple sub-regions is predicted. Because the target power transmission matrix better reflects the power transmission conditions between the multiple sub-regions in the future, data quality is further improved, enabling electronic equipment to improve data processing precision and prediction accuracy within limited computing resources.

[0070] According to an embodiment of the present disclosure, the installed power generation capacity of each of the multiple sub-regions is predicted according to the target power transmission matrix and the power constraint conditions to obtain the target installed power generation capacity of each of the multiple sub-regions, including: predicting the installed power generation capacity of the multiple sub-regions according to the target power transmission matrix and the power constraint conditions to obtain a candidate prediction set of the region, the candidate prediction set including the candidate prediction subsets of each of the multiple sub-regions, the candidate prediction subset including at least one candidate installed power generation capacity; determining the target prediction subset from the candidate prediction set according to the installed power generation capacity condition of the region, the target prediction subset including determining the target installed power generation capacity from at least one candidate installed power generation capacity.

[0071] According to an embodiment of the present disclosure, the candidate prediction subset includes candidate installed power generation capacity of the power system of at least one sub-region. The at least one candidate installed power generation capacity constitutes a solution set for the sub-region.

[0072] According to an embodiment of the present disclosure, a region's installed power generation capacity condition may be that the region's total installed power generation capacity is within a preset range. However, this is not limited to the region's total installed power generation capacity being within the preset range. A region's installed power generation capacity condition may also be that the region's power generation type meets a type condition and the installed power generation capacity of each power generation type is within a planned range.

[0073] According to an embodiment of the present disclosure, the installed power generation capacity of multiple sub-regions is predicted based on the target power transmission matrix and power constraints to obtain a candidate prediction set for the region. Due to the limitations of the power constraints, the impact of noise data can be reduced, and the resource consumption of the computer can be reduced. According to the installed power generation capacity conditions of the region, the target prediction subset is determined from the candidate prediction set, and the target installed power generation capacity of each power system in each sub-region in the future time can be accurately determined, and the prediction data can be refined, so that the carbon emission factor data obtained in subsequent predictions is more accurate.

[0074] According to an embodiment of the present disclosure, the installed power generation capacity condition of a region is that the sum of different types of installed power generation capacities in the region reaches a target threshold.

[0075] According to an embodiment of the present disclosure, the target threshold may be a minimum value of the total installed power generation capacity of the region.

[0076] The installed power generation capacity conditions in the region are as follows:

[0077] (1)

[0078] in, is the total installed power generation capacity of the region, is the candidate installed capacity of power generation type s in sub-region i in the future t years.

[0079] According to an embodiment of the present disclosure, obtaining power transmission planning information of a region includes: obtaining initial power information of the region about future time from a database based on the current time and sub-region identifier; and filtering the initial power information according to preset keywords to obtain power transmission planning information.

[0080] According to an embodiment of the present disclosure, power information about a future time can be determined from a database based on the current time, and initial power information about a region about a future time can be determined from the power information about a future time based on a sub-region identifier.

[0081] According to an embodiment of the present disclosure, the preset keywords may be set according to the power transmission scenario. For example, the preset keywords may include renewable energy, power system, transmission, power generation, etc.

[0082] According to an embodiment of the present disclosure, similarity calculation may be performed between the initial power information and pre-stored historical power transmission planning information, and when the similarity satisfies a similarity threshold, the initial power information is determined as the power transmission planning information.

[0083] According to an embodiment of the present disclosure, by obtaining the initial power information of the region about the future time from the database based on the current time and sub-region identifier, the impact of noise data on subsequent predictions is reduced; the initial power information is filtered according to preset keywords to obtain power transmission planning information, which can increase the speed of information processing and improve computing performance.

[0084] According to an embodiment of the present disclosure, a current power transmission matrix is ​​updated based on power transmission planning information to obtain a target power transmission matrix, including: performing semantic analysis on the power transmission planning information according to the sub-area identifier to obtain an update type for the sub-area; inputting current power transmission data into a prediction model corresponding to the update type to obtain target power transmission data; and updating the current power transmission matrix using the target power transmission data to obtain the target power transmission matrix.

[0085] According to embodiments of the present disclosure, power transmission planning information for a sub-region can be directly identified based on the sub-region identifier. The sub-region power transmission planning information can be preprocessed and subjected to text segmentation to obtain the preprocessed power transmission planning information. This preprocessed power transmission planning information is then input into a large language model to obtain the update type for the sub-region in the power transmission planning information.

[0086] According to an embodiment of the present disclosure, the update type may be a renewable energy cap, an increase in generator sets in the power system, a planning direction for a sub-region, and the like.

[0087] For example, the update type could be an increase in the number of generators in the power system, and the corresponding prediction model could be a linear fitting model. As the number of generators increases, the amount of power transmission data also increases, and the two are linearly related. Since the number of factors affected by the increase in generators in the power system does not exceed the threshold, a simple fitting model can be used for prediction, improving prediction efficiency.

[0088] For example, the update type could be the planning direction of a sub-region, and the corresponding prediction model could be a machine learning model. Because the planning direction of the sub-region changes, the number of factors affected exceeds the threshold, and a simple fitted model cannot be used for prediction, a machine learning model can be used to improve overall prediction accuracy.

[0089] According to an embodiment of the present disclosure, the power constraint conditions include: a balance constraint condition between power consumption and power production of a region, a transmission constraint condition between multiple sub-regions, and a preset constraint condition of power generation type.

[0090] According to an embodiment of the present disclosure, the balance constraint condition represents that the power consumption of a region is equal to the power production.

[0091] Constraint 1: Balance constraint, also known as power demand and supply balance constraint. To ensure power demand, the total power production in the region should be equal to the total power consumption:

[0092] (2)

[0093] is the electricity production of power generation type s in sub-region i in the next t years, is the electricity consumption of sub-region i in the future t years.

[0094] The formula for calculating electricity production is as follows:

[0095] (3)

[0096] is the installed capacity of power generation type s in sub-region i in the next t years, is the average annual utilization hours of power generation type s in sub-region i in the next t years. Specifically, the average annual utilization hours of power generation type in the sub-region at the current time can be calculated based on the average annual utilization hours of power generation type in the sub-region in the next t years.

[0097] According to an embodiment of the present disclosure, the transmission constraint condition characterizes that the sum of the power consumption and power output of a sub-region is equal to the sum of the power production and power input of the sub-region, the power output is the amount of power transmitted from a sub-region to the remaining sub-regions in the region, and the power input is the amount of power transmitted from the remaining sub-regions to the sub-region.

[0098] Constraint 2: Transmission constraint, also known as sub-regional power demand and supply balance constraint. The transmission constraint formula is as follows:

[0099] (4)

[0100] is the amount of electricity input from other sub-regions j to sub-region i in the next t years, is the power output of region i to other sub-region j in the next t years, is the total electricity production of each power generation type in sub-region i in the next t years, is the electricity consumption of sub-region i in the future t years.

[0101] According to an embodiment of the present disclosure, the preset constraint condition of the power generation type includes: the type power generation installed capacity of the power generation type meets the preset threshold condition.

[0102] According to an embodiment of the present disclosure, the preset constraint condition of the power generation type includes at least one of the following: a regulation constraint and a renewable energy power generation upper limit constraint.

[0103] Constraint 3: Regulatory constraints, also known as the regional renewable energy capacity target. T years into the future, the subregion's wind, photovoltaic, and hydropower installed capacity must be no less than the planned target values.

[0104] (5)

[0105] in, is the installed capacity of power generation type s in region i after t years, is the target installed capacity value for power generation type s in sub-region i in the next t years.

[0106] Constraint 4: Renewable energy generation ceiling constraint. The expansion of hydropower, wind power, and photovoltaic capacity is limited by local resource reserves. The installed capacity of renewable energy (wind power, photovoltaic, hydropower) in each sub-region shall not exceed the upper limit of its renewable resource capacity:

[0107] (6)

[0108] in, is the installed capacity of power generation of type in sub-region i, is the upper limit of renewable resources in sub-region i in the future t years.

[0109] Based on the power transmission lines, the power transmission amounts between the sub-areas at the current time, and the like, the current power transmission matrix Tr0 is constructed.

[0110] (7)

[0111] t 12 represents the current power transmission data between sub-area 1 and sub-area 2 at the current time, where n is the number of sub-areas. The current power transmission data may be the current power transmission line capacity.

[0112] The current power transmission matrix is ​​updated based on the regional power transmission planning information to obtain the target power transmission matrix. This power transmission planning information may include at least one of the following: the progress of new transmission line construction in each sub-region, inter-sub-region power transmission plans, and the source of external power for sub-regions with high power demand. This power transmission planning information can be used to determine the power transmission scenario between sub-regions.

[0113] Target power transmission matrix Tr t The formula is as follows:

[0114] (8)

[0115] T （12）t It represents the target power transmission data from Province 1 to Province 2 in the future t years. The target power transmission data can be the power transmission line capacity in the future t years.

[0116] According to an embodiment of the present disclosure, the target power transmission matrix T rt Based on the capacity of UHV and EHV transmission lines and ordinary lines between sub-regions, the output power of each sub-region should be limited. The power transmission within a sub-region is not considered. The input and output power of each sub-region does not exceed the maximum capacity allowed by its line, and the power transmission of each sub-region does not exceed its power production:

[0117] (9)

[0118] (10)

[0119] is the minimum power transmission from sub-area i to other sub-areas j determined according to the target power transmission matrix Trt; is the maximum power transmission capacity from sub-area i to other sub-areas j determined according to the target power transmission matrix Trt.

[0120] According to an embodiment of the present disclosure, the following information can be optimized based on a power system emission factor prediction method based on time-varying power transmission constraints: the target installed power generation capacity of the sub-region in the future, the power generation of the sub-region in the future, the target power input and target power output between multiple sub-regions in the future, the power consumption of the sub-region, the power generation carbon emissions of the sub-region, and the carbon emission factor data of the sub-region in the future.

[0121] Figure 3 The diagram schematically shows the target installed power generation capacity of a sub-region at a future time according to an embodiment of the present disclosure.

[0122] like Figure 3 As shown, there are 30 subregions in total. The target installed capacity of thermal power generation in Subregion 1 is primarily for thermal power generation, resulting in higher carbon emissions in this subregion. However, the target installed capacity of thermal power generation in Subregion 5 is smaller, while the target installed capacity of wind power generation in Subregion 5 is also significant.

[0123] Figure 4 The diagram schematically shows the target power output and target power input of a sub-region at a future time according to an embodiment of the present disclosure.

[0124] like Figure 4As shown, the target power input amount of sub-area 19 is the largest, and the target power output amount of sub-area 2 is the smallest.

[0125] Figure 5 The diagram schematically shows the carbon emissions of power generation in a sub-region in the future according to an embodiment of the present disclosure.

[0126] like Figure 5 As shown, the carbon emissions from power generation in sub-region 28 in the future indicate that sub-region 28 will generate less power using non-renewable energy in the future.

[0127] Figure 6 A schematic diagram schematically illustrates carbon emission factor data of a sub-region at a future time according to an embodiment of the present disclosure.

[0128] like Figure 6 As shown, sub-region 28 will use less non-renewable energy for power generation in the future, and its carbon emission factor data is also less than that of other sub-regions.

[0129] According to the embodiments of the present disclosure, a target power transmission matrix (Trt) is constructed by setting future power transmission scenarios between sub-regions and incorporating dynamic factors such as the progress of new transmission lines and cross-sub-region power transmission planning. This target power transmission matrix dynamically adjusts based on actual information and the evolution of the grid structure, breaking through the static transmission matrix model of fixed or simple extrapolation. It can finely characterize the impact of dynamic factors such as power market changes and transmission channel construction on power transmission between multiple sub-regions, significantly reducing medium- and long-term forecast deviations caused by static constraints and improving the forecast accuracy of emission factor data for the power system.

[0130] According to the embodiments of this disclosure, constraints such as preset generation type constraints, transmission capacity limitations (such as UHV line constraints), and historical transmission continuity conditions are introduced. By introducing planning target values, renewable resource caps, and actual transmission capacity constraints, this approach balances technical feasibility and regulatory compliance. This avoids transmission schemes that might deviate from actual development trajectories in unconstrained methods, ensuring that prediction results meet energy transition goals while also being consistent with regional energy realities and actual grid operating conditions.

[0131] Figure 7 The structural block diagram of the power system emission factor prediction device based on time-varying power transmission constraints according to an embodiment of the present disclosure is schematically shown.

[0132] like Figure 7 As shown, the power system emission factor prediction device 700 based on time-varying power transmission constraints of this embodiment includes an acquisition module 710 , an update module 720 , a first prediction module 730 and a second prediction module 740 .

[0133] Acquisition module 710 is configured to utilize a data interface to acquire regional power transmission planning information from a database. The regional area includes multiple sub-regions having power systems. The power transmission planning information represents the power transmission conditions between the multiple sub-regions at a future time. In one embodiment, acquisition module 710 may be configured to perform operation S210 described above, and will not be further described here.

[0134] Update module 720 is configured to update the current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix. The current power transmission matrix includes multiple elements, each of which represents current power transmission data between multiple sub-regions. The current power transmission data is acquired via sensors deployed in the power system. In one embodiment, update module 720 may be configured to perform operation S220 described above, and will not be further described here.

[0135] First prediction module 730 is configured to predict the installed power generation capacity of each of the multiple sub-regions based on the target power transmission matrix and power constraints, thereby obtaining target installed power generation capacities for each of the multiple sub-regions. The installed power generation capacity represents the power supply performance of the power system in the sub-region. In one embodiment, first prediction module 730 may be configured to perform operation S230 described above, and will not be further described here.

[0136] The second prediction module 740 is used to predict the emission factor data of each of the multiple sub-regions according to the target installed power generation capacity. In one embodiment, the second prediction module 840 can be used to perform the operation S240 described above, which will not be repeated here.

[0137] According to an embodiment of the present disclosure, the first prediction module 830 includes a prediction submodule and a determination submodule. The prediction submodule is used to predict the installed power generation capacity of multiple subregions based on the target power transmission matrix and power constraints to obtain a candidate prediction set for the region, wherein the candidate prediction set includes candidate prediction subsets for each of the multiple subregions, and the candidate prediction subsets include at least one candidate installed power generation capacity; the determination submodule is used to determine a target prediction subset from the candidate prediction set based on the installed power generation capacity conditions of the region, and the target prediction subset includes a target installed power generation capacity determined from the at least one candidate installed power generation capacity.

[0138] According to an embodiment of the present disclosure, the installed power generation capacity condition of a region is that the sum of different types of installed power generation capacities in the region reaches a target threshold.

[0139] According to an embodiment of the present disclosure, the acquisition module 710 includes an acquisition submodule and a screening submodule. The acquisition submodule is used to obtain the initial power information of the region at a future time from the database based on the current time and the subregion identifier; the screening submodule is used to filter the initial power information based on preset keywords to obtain power transmission planning information.

[0140] According to an embodiment of the present disclosure, the update module 720 includes a semantic analysis submodule, an input submodule, and an update submodule. The semantic analysis module is used to perform semantic analysis on the power transmission planning information based on the subregion identifier to obtain the update type of the subregion; the input submodule is used to input the current power transmission data into the prediction model corresponding to the update type to obtain the target power transmission data; and the update submodule is used to update the current power transmission matrix using the target power transmission data to obtain the target power transmission matrix.

[0141] According to an embodiment of the present disclosure, the power constraint conditions include: a balance constraint condition between power consumption and power production of a region, a transmission constraint condition between multiple sub-regions, and a preset constraint condition of power generation type.

[0142] According to an embodiment of the present disclosure, the balance constraint condition represents that the power consumption of a region is equal to the power production.

[0143] According to an embodiment of the present disclosure, the transmission constraint condition characterizes that the sum of the power consumption and power output of a sub-region is equal to the sum of the power production and power input of the sub-region, the power output is the amount of power transmitted from a sub-region to the remaining sub-regions in the region, and the power input is the amount of power transmitted from the remaining sub-regions to the sub-region.

[0144] According to an embodiment of the present disclosure, the preset constraint condition of the power generation type includes: the type power generation installed capacity of the power generation type meets the preset threshold condition.

[0145] According to embodiments of the present disclosure, any multiple modules among the acquisition module 710, update module 720, first prediction module 730, and second prediction module 740 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the acquisition module 710, update module 720, first prediction module 730, and second prediction module 740 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the acquisition module 710 , the update module 720 , the first prediction module 730 , and the second prediction module 740 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0146] Figure 8 The figure schematically shows a block diagram of an electronic device suitable for implementing a method for predicting emission factors of a power system based on time-varying power transmission constraints according to an embodiment of the present disclosure.

[0147] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage unit 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0148] Various programs and data required for the operation of the electronic device 800 are stored in the RAM 803. The processor 801, ROM 802, and RAM 803 are connected to each other via a bus 804. The processor 801 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than the ROM 802 and RAM 803. The processor 801 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0149] According to an embodiment of the present disclosure, electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to bus 804. Electronic device 800 may also include one or more of the following components connected to I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 808 including a hard disk; and a communication section 809 including a network interface card such as a LAN card or modem. Communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to I / O interface 805 as needed. Removable media 811, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 810 as needed, so that computer programs read from the removable media can be installed into storage section 808 as needed.

[0150] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0151] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 802 and / or RAM 803 described above, and / or one or more memories other than ROM 802 and RAM 803.

[0152] Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code causes the computer system to implement the method for predicting power system emission factors based on time-varying power transmission constraints provided in embodiments of the present disclosure.

[0153] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0154] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0155] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 809, and / or installed from a removable medium 811. When the computer program is executed by the processor 801, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0156] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0159] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for predicting power system emission factors based on time-varying power transmission constraints, applied to the service end, characterized in that: The method comprises: Obtaining power transmission planning information of a region from a database using a data interface, wherein the region includes a plurality of sub-regions having power systems, and the power transmission planning information represents power transmission conditions between the plurality of sub-regions in the future; updating a current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix, wherein the current power transmission matrix includes a plurality of elements, each of which is current power transmission data between the plurality of sub-areas, and the current power transmission data is obtained by sensors deployed in the power system; Predicting the installed power generation capacity of each of the plurality of sub-regions according to the target power transmission matrix and the power constraint condition to obtain the target installed power generation capacity of each of the plurality of sub-regions, wherein the installed power generation capacity represents the power supply performance of the power system of the sub-region; According to the target installed power generation capacity, emission factor data of each of the plurality of sub-regions is predicted.

2. The method according to claim 1, characterized in that The step of predicting the installed power generation capacity of each of the plurality of sub-regions according to the target power transmission matrix and the power constraint condition to obtain the target installed power generation capacity of each of the plurality of sub-regions includes: Predicting the installed power generation capacity of the plurality of sub-regions according to the target power transmission matrix and the power constraint condition to obtain a candidate prediction set for the region, wherein the candidate prediction set includes candidate prediction subsets for each of the plurality of sub-regions, and the candidate prediction subsets include at least one candidate installed power generation capacity; According to the installed power generation capacity condition of the region, a target prediction subset is determined from the candidate prediction set, and the target prediction subset includes determining a target installed power generation capacity from at least one of the candidate installed power generation capacities.

3. The method according to claim 2, characterized in that The installed power generation capacity condition of the region is that the sum of different types of installed power generation capacities in the region reaches a target threshold.

4. The method according to claim 1, wherein The acquiring of regional power transmission planning information includes: Based on the current time and the sub-region identifier, obtaining initial power information of the region at the future time from the database; The initial power information is screened according to preset keywords to obtain the power transmission planning information.

5. The method according to claim 4, characterized in that The updating of the current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix includes: Performing semantic analysis on the power transmission planning information according to the sub-area identifier to obtain an update type of the sub-area; Inputting the current power transmission data into a prediction model corresponding to the update type to obtain target power transmission data; The current power transmission matrix is ​​updated using the target power transmission data to obtain the target power transmission matrix.

6. The method according to claim 1, wherein The power constraint conditions include: a balance constraint condition between the power consumption and power production of the region, a transmission constraint condition between the plurality of sub-regions, and a preset constraint condition of power generation type.

7. The method according to claim 6, characterized in that The balance constraint condition indicates that the power consumption of the area is equal to the power production.

8. The method according to claim 6, characterized in that The transmission constraint condition represents that the sum of the power consumption and power output of the sub-area is equal to the sum of the power production and power input of the sub-area, the power output is the amount of power transmitted from the sub-area to the remaining sub-areas in the area, and the power input is the amount of power transmitted from the remaining sub-areas to the sub-area.

9. The method according to claim 1, characterized in that The preset constraint condition of the power generation type includes: the type power generation installed capacity of the power generation type meets the preset threshold condition.

10. A device for predicting power system emission factors based on time-varying power transmission constraints, characterized in that: The device comprises: an acquisition module, configured to acquire power transmission planning information of a region from a database using a data interface, wherein the region includes a plurality of sub-regions having power systems, and the power transmission planning information represents power transmission conditions between the plurality of sub-regions in the future; an updating module, configured to update a current power transmission matrix based on the power transmission planning information to obtain a target power transmission matrix, wherein the current power transmission matrix includes a plurality of elements, each of which is current power transmission data between the plurality of sub-areas, and the current power transmission data is obtained by sensors deployed in the power system; a first prediction module, configured to predict the installed power generation capacity of each of the plurality of sub-regions according to the target power transmission matrix and the power constraint condition, to obtain the target installed power generation capacity of each of the plurality of sub-regions, wherein the installed power generation capacity represents the power supply performance of the power system of the sub-region; The second prediction module is used to predict the emission factor data of each of the plurality of sub-regions according to the target installed power generation capacity.

Images