Electric power carbon emission control method and device and electronic equipment

Abstract

The invention discloses an electric power carbon emission control method and device and electronic equipment. The method comprises the steps that historical power data of a target area are acquired, and the historical power data comprise historical photovoltaic power generation data and historical user power consumption data; based on the historical power data, obtaining a power prediction curve of the target area in a predetermined time period, the power prediction curve including a photovoltaic power generation prediction curve and a user power consumption prediction curve; based on the electric power prediction curve, an expected strategy of the target area in a preset time period is determined, and the expected strategy is used for adjusting at least one of user electricity consumption of a target user in the target area, an electricity storage function of energy storage equipment in the target area and power generation power of power generation equipment in the target area; and based on the expected strategy, controlling the electric power carbon emission of the target area in the preset time period. According to the invention, the technical problems of power energy waste and high carbon emission caused by difficult accurate matching of power supply and demand in related technologies are solved.

Description

Technical Field

[0001] This invention relates to the field of electricity, and more specifically, to a method, apparatus, and electronic device for controlling carbon emissions from electricity generation. Background Technology

[0002] Against the backdrop of increasingly strained energy supplies and escalating environmental problems, traditional power management systems face significant challenges. These systems, often based on static electricity demand forecasts, cannot respond in real-time to weather changes and fluctuations in user behavior, resulting in low utilization rates of clean energy sources such as solar and wind power. Particularly in the industrial sector, there is a significant peak-valley difference in enterprise electricity consumption, with a common phenomenon of excess electricity at night and insufficient electricity during the day. Furthermore, the issue of carbon emissions from electricity is receiving increasing attention. The large-scale use of fossil fuels has not only accelerated global warming but also caused a series of environmental problems, including air pollution. While existing power management systems attempt to reduce their carbon footprint through optimized dispatching, their effectiveness is often unsatisfactory due to a lack of accurate forecasting capabilities and flexible policy adjustment mechanisms. In traditional power systems, the lack of precise supply and demand matching mechanisms leads to low utilization rates of clean energy and high carbon emissions.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for controlling carbon emissions from electricity, in order to at least solve the technical problem that related technologies are unable to accurately match electricity supply and demand, resulting in waste of electricity and high carbon emissions.

[0005] According to one aspect of the present invention, a method for controlling carbon emissions from electricity is provided, comprising: acquiring historical electricity data of a target area, wherein the historical electricity data includes historical photovoltaic power generation data and historical user electricity consumption data; acquiring an electricity forecast curve for the target area over a predetermined time period based on the historical electricity data, wherein the electricity forecast curve includes a photovoltaic power generation forecast curve and a user electricity consumption forecast curve; determining a desired strategy for the target area over the predetermined time period based on the electricity forecast curve, wherein the desired strategy is used to adjust at least one of the following: the user electricity consumption of a target user in the target area, the energy storage function of an energy storage device in the target area, and the power generation capacity of a power generation device in the target area; and controlling the carbon emissions from electricity in the target area over the predetermined time period based on the desired strategy.

[0006] Optionally, obtaining the power prediction curve of the target area within a predetermined time period based on the historical power data includes: inputting the historical power data into a target prediction model to obtain multiple power prediction values ​​for the target area within the predetermined time period, wherein the multiple power prediction values ​​include hourly power prediction values ​​for the target area within the predetermined time period, and the power prediction values ​​include photovoltaic power generation prediction values ​​and user electricity consumption prediction values; constructing a power fitting polynomial for the target area within the predetermined time period based on the multiple power prediction values, wherein the power fitting polynomial includes a photovoltaic power generation fitting polynomial and a user electricity consumption fitting polynomial; and obtaining the power prediction curve based on the power fitting polynomial.

[0007] Optionally, constructing a power fitting polynomial for the target area during the predetermined time period based on the plurality of power forecast values ​​includes: determining the order of the power fitting polynomial based on the number of the plurality of power forecast values; determining the polynomial coefficients of the power fitting polynomial based on the numerical values ​​corresponding to the plurality of power forecast values; and constructing the power fitting polynomial based on the order and the polynomial coefficients.

[0008] Optionally, determining the desired strategy for the target area during the predetermined time period based on the power forecast curve includes: obtaining an early warning indicator for the target area during the predetermined time period based on the power forecast curve; and determining the desired strategy for the target area during the predetermined time period based on the early warning indicator.

[0009] Optionally, obtaining the early warning indicator for the target area during the predetermined time period based on the power forecast curve includes: obtaining the early warning indicator based on the power forecast curve using the following formula:

[0010] ,

[0011] In the formula, This indicates the aforementioned warning indicator. This represents the photovoltaic power generation prediction curve. This represents the user's electricity consumption prediction curve. This indicates the time within the predetermined time period. The integer represents the hour within the predetermined time period.

[0012] Optionally, determining the desired strategy for the target area during the predetermined time period based on the warning indicator includes: if the warning indicator is greater than a predetermined threshold, determining the desired strategy as sending a first message to the target user and / or sending a second message to the energy storage device, wherein the first message is used to increase the user's electricity consumption and the second message is used to activate the energy storage function of the energy storage device; or, if the warning indicator is less than or equal to the predetermined threshold, determining the desired strategy as sending a third message to the target user and / or sending a fourth message to the power generation device, wherein the third message is used to reduce the user's electricity consumption and the fourth message is used to increase the power generation capacity of the power generation device.

[0013] According to another aspect of the present invention, an electricity carbon emission control device is provided, comprising: a first acquisition module, configured to acquire historical electricity data of a target area, wherein the historical electricity data includes historical photovoltaic power generation data and historical user electricity consumption data; a second acquisition module, configured to acquire, based on the historical electricity data, an electricity prediction curve of the target area over a predetermined time period, wherein the electricity prediction curve includes a photovoltaic power generation prediction curve and a user electricity consumption prediction curve; a determination module, configured to determine, based on the electricity prediction curve, a desired strategy for the target area during the predetermined time period, wherein the desired strategy is used to adjust at least one of the following: the user electricity consumption of a target user in the target area, the energy storage function of an energy storage device in the target area, and the power generation capacity of a power generation device in the target area; and a control module, configured to control the electricity carbon emissions of the target area during the predetermined time period based on the desired strategy.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the electricity carbon emission control method described in any one of the above claims.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program, when running, executes the electricity carbon emission control method described in any one of the preceding claims.

[0016] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of any of the methods described in the method for controlling carbon emissions from electricity.

[0017] In this embodiment of the invention, historical power data of the target area is acquired, including historical photovoltaic power generation data and historical user electricity consumption data. Based on the historical power data, a power prediction curve for the target area within a predetermined time period is obtained, including a photovoltaic power generation prediction curve and a user electricity consumption prediction curve. Based on the power prediction curve, a desired strategy for the target area within the predetermined time period is determined, wherein the desired strategy is used to adjust at least one of the following: the user electricity consumption of the target users in the target area, the energy storage function of the energy storage devices in the target area, and the power generation capacity of the power generation devices in the target area. Based on the desired strategy, the carbon emissions of electricity in the target area within the predetermined time period are controlled, achieving unified coordination between the user side, the energy storage side, and the power generation side, ensuring the balance of power supply and demand in the target area. This achieves the technical effect of refined management of power resources and reduction of carbon emissions of electricity, thereby solving the technical problem that related technologies are difficult to accurately match power supply and demand, resulting in power energy waste and high carbon emissions. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of a method for controlling carbon emissions from electricity according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of a photovoltaic power generation forecast curve and a user electricity consumption forecast curve according to an optional embodiment of the present invention;

[0021] Figure 3 This is a structural block diagram of an electricity carbon emission control device according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0025] Carbon emissions from electricity generation refer to the total amount of carbon dioxide produced during the production, transmission, and use of electricity through the combustion of fossil fuels (such as coal, oil, and natural gas) or other activities. These emissions primarily originate from thermal power plants, with coal and natural gas being the most common fuels, releasing large amounts of carbon dioxide into the atmosphere during combustion. Furthermore, losses during electricity transmission also indirectly contribute to carbon emissions, as the electricity lost during transmission is essentially generated but not effectively utilized energy, and its production process may also have generated carbon emissions.

[0026] In the current field of distributed generation and electricity consumption management, traditional power dispatching systems face significant challenges, particularly in the efficient utilization of clean energy and dynamic carbon emission monitoring. Related technologies rely on static analysis of historical data, lacking real-time response capabilities to complex environmental factors (such as weather changes), resulting in insufficient prediction accuracy and difficulty in precisely matching photovoltaic power supply with user demand. Furthermore, low user participation and overly rigid early warning mechanisms and energy storage strategy adjustments fail to dynamically adapt to changes in electricity supply and demand. This not only limits the efficient utilization of clean energy but may also lead to unnecessary electricity waste and increased carbon emissions. More importantly, generation-side strategy optimization often neglects interaction with the external grid, lacking effective ways to optimize external power supply strategies while meeting internal needs. These technological bottlenecks constrain the overall efficiency improvement of distributed generation systems and hinder the power industry's transformation towards low-carbon and intelligent systems.

[0027] According to an embodiment of the present invention, an embodiment of a method for controlling carbon emissions from electricity is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a method for controlling carbon emissions from electricity according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step S102: Obtain historical power data for the target area, including historical photovoltaic power generation data and historical user electricity consumption data.

[0030] As an optional embodiment, the execution subject of this method can be a terminal or a server for controlling electricity carbon emissions. When applied to a terminal, electricity carbon emission control can be easily implemented; when applied to a server, the server's abundant computing resources can be utilized, allowing for more accurate control of electricity carbon emissions. The terminal can be of various types, such as a mobile terminal with certain computing capabilities or a fixed computer device with identification capabilities. Similarly, the server can be of various types, such as a local server or a virtual cloud server. Depending on computing power, it can be a single computer device or a computer cluster integrating multiple computer devices.

[0031] As an optional implementation, various methods can be used to acquire historical electricity data for a target area. For example, electricity data for the target area can be continuously collected through smart meters or various sensors installed in the target area, including but not limited to hourly photovoltaic power generation output and user electricity consumption. The target area can be of various types, such as industrial parks, commercial areas, or residential areas. Acquiring historical photovoltaic power generation data and historical user electricity consumption data for the target area helps the system learn and understand the periodicity and changing trends of electricity supply and demand within the target area.

[0032] Step S104: Based on historical power data, obtain the power forecast curve for the target area within a predetermined time period, wherein the power forecast curve includes the photovoltaic power generation forecast curve and the user power consumption forecast curve.

[0033] As an optional implementation, various methods can be used to obtain the power forecast curve for a target area over a predetermined time period based on historical power data. For example, historical power data can be input into the target prediction model to obtain multiple power forecast values ​​for the target area over the predetermined time period. These multiple power forecast values ​​include hourly power forecast values ​​for the target area within the predetermined time period, and include photovoltaic power generation forecast values ​​and user electricity consumption forecast values. Based on these multiple power forecast values, a power fitting polynomial for the target area over the predetermined time period is constructed, including a photovoltaic power generation fitting polynomial and a user electricity consumption fitting polynomial. Based on the power fitting polynomial, the power forecast curve is obtained. Through the target prediction model, accurate predictions of power demand and photovoltaic supply for the target area over a predetermined time period can be achieved. By fitting polynomials, the corresponding photovoltaic power generation forecast curve and user electricity consumption forecast curve can be made smoother, better reflecting the time-series characteristics of power data, which is beneficial for subsequent strategy formulation.

[0034] As an optional implementation, various methods can be used when inputting historical power data into the target prediction model to obtain multiple power prediction values ​​for the target area over a predetermined time period. For example, advanced machine learning algorithms, such as Long Short-Term Memory networks, Recurrent Neural Networks, or other models suitable for time series analysis, can be used to combine historical power data to predict power demand and photovoltaic power generation for a future time period, such as hourly photovoltaic power output and user power consumption over the next day or week.

[0035] As an optional implementation, various methods can be used when constructing a power fitting polynomial for a target area over a predetermined time period based on multiple power forecast values. For example, the order of the power fitting polynomial can be determined based on the number of power forecast values; the polynomial coefficients can be determined based on the values ​​corresponding to each of the multiple power forecast values; and the power fitting polynomial can be constructed based on the order and polynomial coefficients. When the predetermined time period is one day, the power forecast values ​​for each hour within that day can be obtained. In this case, the number of power forecast values ​​is 24, and the order of the polynomial can be determined to be 23, ensuring that the construction of the power fitting polynomial conforms to the data point distribution while avoiding overfitting. When determining the polynomial coefficients, the least squares method or other optimization algorithms can be used based on the values ​​corresponding to each of the multiple power forecast values. The number of polynomial coefficients is the same as the order of the polynomial, and these polynomial coefficients can represent the weights of each order term in the polynomial. Constructing the power fitting polynomial based on the order and polynomial coefficients can improve the objectivity and accuracy of the power fitting polynomial.

[0036] Step S106: Based on the power forecast curve, determine the expected strategy for the target area during a predetermined time period, wherein the expected strategy is used to adjust at least one of the following: the electricity consumption of the target users in the target area, the energy storage function of the energy storage device in the target area, and the power generation capacity of the power generation device in the target area.

[0037] As an optional implementation, based on electricity forecasting curves, periods of electricity surplus and shortage can be automatically identified, and corresponding expected strategies can be formulated. These expected strategies can involve aspects such as electricity use, storage, and production. For example, by adjusting the electricity consumption of target users in a target area, the energy storage capacity of energy storage devices in the target area, and the power generation capacity of power generation equipment in the target area, a balance between electricity supply and demand in the target area can be achieved within a predetermined time period. This avoids both electricity waste and the disruption of normal production and life for target users due to power shortages, ultimately achieving the goal of reducing carbon emissions from electricity.

[0038] As an optional implementation, various methods can be used to determine the expected strategy for a target area within a predetermined time period based on the power forecast curve. For example, an early warning indicator for the target area within the predetermined time period can be obtained based on the power forecast curve; and the expected strategy for the target area within the predetermined time period can be determined based on the early warning indicator. Various methods can be used to analyze the power forecast curve. For instance, obtaining the early warning indicator for the target area within the predetermined time period can be used to measure the difference between photovoltaic power generation and user electricity consumption within the forecast period, more intuitively reflecting the surplus or shortage of power supply and demand within the forecast period. Various methods can also be used to utilize the early warning indicator. For example, a threshold can be set; when the early warning indicator is greater than the threshold, it indicates a power surplus; when the early warning indicator is less than the threshold, it indicates a power shortage. This facilitates a more accurate judgment of the surplus or shortage of power supply and demand within the forecast period, thereby more accurately determining the expected strategy for the target area within the predetermined time period based on the surplus or shortage of power supply and demand.

[0039] As an optional implementation, various methods can be used to obtain early warning indicators for a target area within a predetermined time period based on power forecast curves. For example, early warning indicators can be obtained based on power forecast curves using the following formula:

[0040]

[0041] In the formula, Indicates early warning indicators, This represents the photovoltaic power generation forecast curve. This represents the user's electricity consumption forecast curve. Indicates the time within the scheduled time period. The integer represents the hour within a predetermined time period. The introduction of early warning indicators enables the system to quantitatively assess the power supply and demand status, thereby more accurately adjusting user power consumption strategies, energy storage device operation strategies, and power generation device generation strategies.

[0042] As an optional implementation, various methods can be used to determine the desired strategy for a target area within a predetermined time period based on early warning indicators. For example, if the early warning indicator is greater than a predetermined threshold, the desired strategy can be determined to be sending a first message to the target user and / or sending a second message to the energy storage device. The first message is used to increase the target user's electricity consumption, and the second message is used to activate the energy storage function of the energy storage device. Alternatively, if the early warning indicator is less than or equal to the predetermined threshold, the desired strategy can be determined to be sending a third message to the target user and / or sending a fourth message to the power generation equipment. The third message is used to reduce the target user's electricity consumption, and the fourth message is used to increase the power generation capacity of the power generation equipment. When the early warning indicator is greater than the predetermined threshold, it means that the predicted photovoltaic power generation exceeds the predicted user electricity consumption, i.e., there is a power surplus. In this case, users can be encouraged to increase their electricity consumption, and the energy storage device can be notified to activate its energy storage function to store the excess power and avoid power waste. Conversely, when the early warning indicator is less than or equal to the predetermined threshold, it indicates that there may be a power shortage, i.e., insufficient power supply. In this case, users can be prompted to conserve electricity, and the power generation equipment can be encouraged to increase its power generation capacity to ensure the stable operation of the power system.

[0043] Step S108: Based on the desired strategy, control the carbon emissions of electricity in the target area within a predetermined time period.

[0044] As an optional embodiment, by implementing the aforementioned desired strategy, the operating status of the power system in the target area can be monitored and regulated in real time. By adjusting user electricity consumption plans, optimizing the charging and discharging strategies of energy storage devices, and starting or stopping power generation equipment in a timely manner, the power supply and demand relationship in the target area can be effectively balanced, the utilization rate of clean electricity can be maximized, the dependence on non-renewable energy can be reduced, and the key role in reducing carbon emissions from electricity and promoting the application of green energy can be played.

[0045] By acquiring historical power data for the target area, including historical photovoltaic power generation data and historical user electricity consumption data; based on the historical power data, obtaining power forecast curves for the target area over a predetermined time period, including photovoltaic power generation forecast curves and user electricity consumption forecast curves; based on the power forecast curves, determining the desired strategy for the target area over the predetermined time period, wherein the desired strategy is used to adjust at least one of the following: the user electricity consumption of the target users in the target area, the energy storage function of the energy storage devices in the target area, and the power generation capacity of the power generation devices in the target area; based on the desired strategy, controlling the carbon emissions of electricity in the target area over the predetermined time period, achieving unified coordination between the user side, the energy storage side, and the power generation side, ensuring the balance of power supply and demand in the target area, thereby realizing the technical effects of refined management of power resources and reduction of carbon emissions of electricity, and thus solving the technical problem that related technologies are difficult to accurately match power supply and demand, resulting in power energy waste and high carbon emissions.

[0046] Based on the above embodiments and optional embodiments, an optional implementation method is provided. In this optional implementation method, a method for optimizing the power generation strategy of a distributed generation system is proposed. The method includes the following processing.

[0047] S1, Predictive Analysis. The system uses a predictive algorithm to predict the photovoltaic power generation and user electricity consumption at key time points (every hour) each day, obtaining 24 prediction points. Then, the prediction curves of photovoltaic power generation and user electricity consumption are fitted by interpolation.

[0048] S1.1, collect historical photovoltaic power generation data and user historical electricity consumption data, including daily and hourly power generation and electricity consumption.

[0049] S1.2 uses a deep learning model to predict the photovoltaic power generation and user electricity consumption every day and every hour in the future.

[0050] S1.3, interpolation is used to generate smooth prediction curves between prediction points. For example, a polynomial fitting method can be used. An appropriate polynomial order (e.g., a 23rd degree polynomial) is selected; a higher order can capture data trends more precisely, but the risk of overfitting also increases. The system can automatically select the optimal polynomial order based on the time series characteristics of power generation and consumption. The fitting function is constructed, and the polynomial function form is set as follows: , where t is a time variable, ranging from 0 to 23 to represent the 24 hours in a day; (i = 0, 1, 2, ..., 23) are the polynomial coefficients to be determined. Finally, the least squares method is used to determine the polynomial coefficients.

[0051] S2, Curve Comparison. The predicted photovoltaic (PV) power generation curve is compared with the user electricity consumption curve. Periods when PV power generation exceeds user electricity consumption are recorded; these periods are when the predicted PV power generation curve is above the predicted user electricity consumption curve. An early warning index I is constructed. The fitted predicted PV curve is denoted as F(t), and the fitted user electricity consumption curve is denoted as Z(t). The early warning indicator I indicates that the predicted photovoltaic power generation exceeds the predicted user electricity consumption within each hourly time period. The value of 'a' is an integer, indicating the starting point for calculating the electricity surplus. Figure 2 This is a schematic diagram of a photovoltaic power generation forecast curve and a user electricity consumption forecast curve according to an optional embodiment of the present invention, such as... Figure 2 As shown, there will be a surplus in photovoltaic power generation between 13:00 and 15:00. This information will be recorded and sent to companies in the park.

[0052] S3, Strategy Guidance. This includes optimization of user-side electricity consumption strategies, optimization of energy storage strategies for energy storage devices, and optimization of generation strategies for the generation side.

[0053] S3.1, User-side power consumption strategy optimization: It is easy to see that when I is greater than 0, the photovoltaic power generation in that hour is sufficient for the park's power consumption. The system will record the time period when I is greater than 0 and issue a warning to users one or more days in advance, reminding users to use more electricity during that period and to arrange production reasonably.

[0054] S3.2, Energy storage strategy optimization: Specifically, the system also uses the early warning indicator I. When I is greater than 0, the photovoltaic power generation in that hour is sufficient for the park's electricity consumption. The system will record the time period when I is greater than 0 and issue an early warning to the energy storage staff one or more days in advance, reminding them to store more electricity during that period.

[0055] S3.3, Power generation strategy optimization on the power generation side: Specifically, the system also uses the early warning indicator I. When I is less than 0, the photovoltaic power generation in that hour is insufficient for the park's electricity consumption. The system will record the time period when I is less than 0 and issue an early warning to the distributed photovoltaic power generation facilities one or more days in advance, reminding staff to increase the photovoltaic power generation during that period.

[0056] The following technical effects can be achieved through the above-mentioned distributed generation system power generation strategy optimization methods: (1) Reduce carbon emissions: By optimizing electricity use and storage, the dependence on fossil fuels can be reduced, thus lowering carbon emissions. (2) Improve green power utilization: Increase the use of self-built distributed photovoltaic power and improve the utilization rate of green energy. (3) Win-win for both economy and environment: It can save electricity costs and achieve emission reduction targets, thus promoting sustainable development.

[0057] According to an embodiment of the present invention, an electricity carbon emission control device is provided. Figure 3This is a structural block diagram of an electricity carbon emission control device according to an embodiment of the present invention, such as... Figure 3 As shown, the device includes: a first acquisition module 302, a second acquisition module 304, a determination module 306, and a control module 308. The device will be described below.

[0058] The first acquisition module 302 is used to acquire historical power data of the target area, wherein the historical power data includes historical photovoltaic power generation data and historical user electricity consumption data; the second acquisition module 304 is connected to the first acquisition module 302 and is used to acquire the power forecast curve of the target area for a predetermined time period based on the historical power data, wherein the power forecast curve includes the photovoltaic power generation forecast curve and the user electricity consumption forecast curve; the determination module 306 is connected to the second acquisition module 304 and is used to determine the expected strategy of the target area for the predetermined time period based on the power forecast curve, wherein the expected strategy is used to adjust at least one of the following: the user electricity consumption of the target users in the target area, the energy storage function of the energy storage device in the target area, and the power generation capacity of the power generation device in the target area; the control module 308 is connected to the determination module 306 and is used to control the electricity carbon emissions of the target area for the predetermined time period based on the expected strategy.

[0059] It should be noted that the first acquisition module 302, the second acquisition module 304, the determination module 306 and the control module 308 mentioned above correspond to steps S102 to S108 in the embodiments. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments.

[0060] As an optional embodiment, the second acquisition module 304 includes: a first acquisition unit, a construction unit, and a second acquisition unit. The first acquisition unit is used to input historical power data into a target prediction model to acquire multiple power prediction values ​​for a target area within a predetermined time period. These multiple power prediction values ​​include hourly power prediction values ​​for the target area within the predetermined time period, and the power prediction values ​​include photovoltaic power generation prediction values ​​and user electricity consumption prediction values. The construction unit, connected to the first acquisition unit, is used to construct a power fitting polynomial for the target area within the predetermined time period based on the multiple power prediction values. This power fitting polynomial includes a photovoltaic power generation fitting polynomial and a user electricity consumption fitting polynomial. The second acquisition unit, connected to the construction unit, is used to acquire a power prediction curve based on the power fitting polynomial.

[0061] As an optional embodiment, the above-mentioned construction unit includes: a first determining subunit, a second determining subunit, and a construction subunit. The first determining subunit is used to determine the order of the power fitting polynomial based on the number of multiple power prediction values; the second determining subunit, connected to the first determining subunit, is used to determine the polynomial coefficients of the power fitting polynomial based on the numerical values ​​corresponding to the multiple power prediction values; and the construction subunit, connected to the second determining subunit, is used to construct the power fitting polynomial based on the order and the polynomial coefficients.

[0062] As an optional embodiment, the determining module 306 includes a third acquisition unit and a determining unit. The third acquisition unit is used to acquire early warning indicators for a target area within a predetermined time period based on a power forecast curve; the determining unit, connected to the third acquisition unit, is used to determine the desired strategy for the target area within the predetermined time period based on the early warning indicators.

[0063] As an optional embodiment, the third acquisition unit mentioned above includes: an acquisition subunit. The acquisition subunit is used to acquire early warning indicators based on the power forecast curve using the following formula:

[0064] ,

[0065] In the formula, Indicates early warning indicators, This represents the photovoltaic power generation forecast curve. This represents the user's electricity consumption forecast curve. Indicates the time within the scheduled time period. It is an integer, representing the hour within the predetermined time period.

[0066] As an optional embodiment, the aforementioned determining unit includes a third determining subunit and a fourth determining subunit. The third determining subunit is configured to determine, when the warning indicator is greater than a predetermined threshold, the desired strategy is to send a first message to the target user and / or send a second message to the energy storage device, wherein the first message is used to increase the target user's electricity consumption, and the second message is used to activate the energy storage function of the energy storage device. The fourth determining subunit is configured to determine, when the warning indicator is less than or equal to the predetermined threshold, the desired strategy is to send a third message to the target user and / or send a fourth message to the power generation device, wherein the third message is used to reduce the target user's electricity consumption, and the fourth message is used to increase the power generation capacity of the power generation device.

[0067] According to an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the electricity carbon emission control method described above.

[0068] According to an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the electricity carbon emission control method described in any one of the preceding embodiments.

[0069] According to an embodiment of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0070] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0071] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0075] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0076] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling carbon emissions from electricity generation, characterized in that, include: Obtain historical power data for the target area, wherein the historical power data includes historical photovoltaic power generation data and historical user electricity consumption data; Based on the historical power data, the power forecast curve for the target area during a predetermined time period is obtained, wherein the power forecast curve includes a photovoltaic power generation forecast curve and a user power consumption forecast curve. Based on the power forecast curve, a desired strategy for the target area during the predetermined time period is determined, wherein the desired strategy is used to adjust at least one of the following: the electricity consumption of target users in the target area, the energy storage function of the energy storage device in the target area, and the power generation capacity of the power generation device in the target area. Based on the desired strategy, the amount of electricity carbon emissions in the target area during the predetermined time period is controlled.

2. The method according to claim 1, characterized in that, The step of obtaining the power forecast curve for the target area over a predetermined time period based on the historical power data includes: The historical power data is input into the target prediction model to obtain multiple power prediction values ​​for the target area during the predetermined time period. The multiple power prediction values ​​include the power prediction values ​​for each hour of the target area during the predetermined time period, and the power prediction values ​​include photovoltaic power generation prediction values ​​and user electricity consumption prediction values. Based on the multiple power forecast values, a power fitting polynomial for the target area during the predetermined time period is constructed, wherein the power fitting polynomial includes a photovoltaic power generation fitting polynomial and a user power consumption fitting polynomial. The power prediction curve is obtained based on the power fitting polynomial.

3. The method according to claim 2, characterized in that, The step of constructing a power fitting polynomial for the target area during the predetermined time period based on the multiple power forecast values ​​includes: The order of the power fitting polynomial is determined based on the number of the multiple power prediction values. Based on the numerical values ​​corresponding to the multiple power prediction values, the polynomial coefficients of the power fitting polynomial are determined. Based on the order and the polynomial coefficients, the power fitting polynomial is constructed.

4. The method according to claim 1, characterized in that, The step of determining the desired strategy for the target area during the predetermined time period based on the power forecast curve includes: Based on the power forecast curve, obtain the early warning indicators for the target area during the predetermined time period; Based on the warning indicators, the desired strategy for the target area during the predetermined time period is determined.

5. The method according to claim 4, characterized in that, The step of obtaining the early warning indicators for the target area during the predetermined time period based on the power forecast curve includes: Based on the power forecast curve, the early warning indicator is obtained using the following formula: ， In the formula, This indicates the aforementioned warning indicator. This represents the photovoltaic power generation prediction curve. This represents the user's electricity consumption prediction curve. This indicates the time within the predetermined time period. The integer represents the hour within the predetermined time period.

6. The method according to claim 4, characterized in that, The step of determining the desired strategy for the target area during the predetermined time period based on the early warning indicators includes: If the warning indicator exceeds a predetermined threshold, the desired strategy is determined to be sending a first message to the target user and / or sending a second message to the energy storage device, wherein the first message is used to increase the target user's electricity consumption, and the second message is used to activate the energy storage function of the energy storage device; or... If the warning indicator is less than or equal to the predetermined threshold, the desired strategy is determined to be to send a third message to the target user and / or to send a fourth message to the power generation equipment, wherein the third message is used to reduce the user's electricity consumption and the fourth message is used to increase the power generation capacity of the power generation equipment.

7. A device for controlling carbon emissions from electricity generation, characterized in that, include: The first acquisition module is used to acquire historical power data of the target area, wherein the historical power data includes historical photovoltaic power generation data and historical user electricity consumption data; The second acquisition module is used to acquire the power prediction curve of the target area within a predetermined time period based on the historical power data, wherein the power prediction curve includes a photovoltaic power generation prediction curve and a user power consumption prediction curve. The determination module is used to determine the expected strategy of the target area during the predetermined time period based on the power forecast curve, wherein the expected strategy is used to adjust at least one of the following: the electricity consumption of the target users in the target area, the energy storage function of the energy storage device in the target area, and the power generation capacity of the power generation device in the target area; A control module is used to control the amount of electricity carbon emissions in the target area during a predetermined time period based on the desired strategy.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device containing the computer-readable storage medium to perform the electricity carbon emission control method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program executes the electricity carbon emission control method according to any one of claims 1 to 6 when it runs.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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