Wind power generation system scheduling method considering carbon emission and related device
By collecting and processing data from wind farms, meteorology, and power grids, a day-ahead scheduling optimization model that takes carbon emissions into account is constructed. This solves the problem that existing wind power generation systems struggle to balance economic efficiency and low carbon emissions, and achieves low-carbon scheduling optimization for wind power generation systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN THERMAL POWER RES INST CO LTD
- Filing Date
- 2026-01-09
- Publication Date
- 2026-04-24
AI Technical Summary
Existing power generation systems have failed to prioritize carbon emissions as a core optimization objective, making it difficult to balance economic efficiency and low carbon emissions after large-scale grid connection of wind power. Furthermore, the intermittency and volatility of wind power increase the difficulty of system scheduling and weaken the benefits of carbon emission reduction.
Data from wind farms, meteorology, and power grids are collected. Through validity verification and correction of poor data, a day-ahead scheduling optimization model that takes carbon emissions into account is constructed. Combined with a hierarchical constraint system and a rolling optimization feedback mechanism, the scheduling of wind power generation systems is optimized to reduce carbon emissions and operating costs.
It achieves a synergistic optimization that balances economic efficiency and low carbon emissions while ensuring the safety and stability of the power grid, thereby reducing the carbon emissions and operating costs of wind power generation systems and improving wind power absorption capacity and dispatch accuracy.
Smart Images
Figure CN121923280A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for scheduling wind power generation systems that takes carbon emissions into account. Background Technology
[0002] Due to its intermittent and fluctuating nature, wind power generation systems need to be flexibly combined with other power generation methods to build a stable and reliable low-carbon power network. By combining with thermal power generation, it can quickly compensate for power shortages when there is no wind, while significantly reducing the operating time and total emissions of coal-fired power plants. When complemented by solar photovoltaic power, it can smooth the intraday power generation curve. More importantly, by connecting to hydropower, energy storage batteries, and smart grid systems, it can transform uncontrollable wind energy into stable and dispatchable green electricity.
[0003] However, existing power generation systems mostly focus on minimizing total power generation cost or coal consumption as a single objective, failing to incorporate "carbon cost" as a core optimization objective into the dispatch model. There is a disconnect between economic dispatch and low-carbon dispatch. As a major energy source, wind power's large-scale grid connection makes it difficult to balance power system dispatch. Furthermore, due to the inherent intermittency, volatility, and anti-peak-shaving characteristics of wind energy, the system must reserve a large amount of flexible adjustment resources to balance power fluctuations, which may weaken the carbon emission reduction benefits of wind energy. It is impossible to simultaneously achieve both economic efficiency and low carbon emissions. Summary of the Invention
[0004] In view of the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide a wind power generation system dispatching method and related apparatus that takes into account carbon emissions.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for dispatching a wind power generation system that takes carbon emissions into account, comprising: S1, collect wind turbine operating parameters, weather forecast data, power grid operation status data and carbon trading market data from wind power plants as the initial dataset; S2, the data in the initial dataset is preprocessed using methods of validity verification and bad data correction to obtain the target dataset; S3, based on carbon emission costs, constructs a day-ahead dispatch optimization model that considers carbon emissions to reduce carbon emissions and operating costs. Specifically, a mathematical model is constructed that comprehensively considers the constraints of generator power generation, fuel consumption, and carbon emission, with economic cost and carbon emission reduction targets as optimization objectives. Under the premise of meeting load demand, the power system reduces operating costs and carbon emissions. The objective function formula is as follows:
[0006] in, This represents the total number of scheduling periods; This represents the total number of generator sets. For scheduling periods; Number the generator set; Let be the unit power generation cost of the i-th generator set; Let be the power output of the i-th generator unit during time period t; Let be the unit carbon emission cost coefficient of the i-th generator set; Let be the carbon emissions of the i-th generator unit during time period t; S4. Construct a hierarchical constraint system and use multiple types of constraints to limit power grid security; S5, based on real-time power grid operation data and carbon emission constraints, yields the optimal low-carbon dispatch scheme.
[0007] A further improvement of this invention is that, in S1, the collection of wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data from wind power plants as the initial dataset includes: employing multi-source heterogeneous data fusion technology, during the data acquisition phase, comprehensively collecting multi-dimensional data through smart meters, weather monitoring equipment, generator set distributed control systems, and power grid monitoring and data acquisition systems, covering load forecasting, power generation forecasting, generator set technical parameters, and power grid structure data, while simultaneously constructing a carbon emission factor database to meet the data requirements of the scheduling method, and using the obtained data as the initial dataset.
[0008] A further improvement of this invention is that, in S2, the data in the initial dataset is preprocessed using methods for validity verification and bad data correction to obtain the target dataset, including: Step S21: Use a validity verification method to filter out outliers in the initial dataset; Based on the physical laws of the power grid, constraints are set for data filtering, specifically for unit output data. It must meet its minimum output. and maximum output The limitation, namely For load data L, compare it with historical load data for the same period. In comparison, it needs to meet the following requirements. ,in The set reasonable deviation coefficient is used to trigger the abnormal data identification mechanism when the unit output data and load data exceed the set value, and the data in question is removed.
[0009] A further improvement of the present invention is that, in step S22, abnormal data is processed by means of bad data correction. During power system dispatching, measurement data is easily affected by communication interference and equipment failures, resulting in outliers. The presence of abnormal data can seriously affect the accuracy and reliability of the dispatching model. A weighted average interpolation method is used to process outliers. The specific formula for the weighted average interpolation method is as follows:
[0010] in, This represents the new value of the i-th position after calculation and update; Indicates the i-th The weight of a single position; Indicates the i-th The original value at one position; The weights are for the (i+1)th position; These are the original values at position i+1.
[0011] A further improvement of this invention is that, in step S4, a hierarchical constraint system is constructed, and multiple types of constraints are used to limit grid security, including: Step S41, based on the principle of instantaneous power balance of the power system, a dynamic balance equation is constructed between the generation side and the load side, and power balance constraints are used to force conventional unit power generation, wind power output, energy storage charging and discharging power to maintain real-time balance with system load and grid losses. The power balance constraint formula is as follows:
[0012] in, The number of thermal power generating units; Let be the active power output of the i-th thermal power generating unit at time t; This represents the active power output of the wind turbine generator at time t. The predicted load power of the system at time t; The set of all moments within the scheduling period; The power balance equation holds true throughout the scheduling period.
[0013] A further improvement of the present invention is that, in step S42, the unit operation constraints include the upper and lower limits of unit output and the ramp rate. The upper and lower limits of output are set according to the unit's technical parameters. These limits constrain the unit's output to stay within the technically permissible range, preventing overload or underload operation. The formulas for the upper and lower limits of output are as follows:
[0014] in, Let i be the operating state variable of device i at time t; The minimum active power output of device i; The actual active power output of device i at time t; The maximum active power output of device i; This holds true for all devices i and all times t.
[0015] A further improvement of this invention is that, in S5, based on real-time grid operation data and carbon emission constraints, the optimal low-carbon dispatch scheme is obtained by: continuously integrating real-time grid operation data and ultra-short-term wind power forecasts to sense system status and plan deviations; subsequently, based on the day-ahead plan, a fast rolling optimization model is initiated to dynamically solve the optimal power generation adjustment scheme in the shortest future time period under carbon emission and grid security constraints; finally, the dispatch instructions are executed through the automatic control system, forming a real-time closed loop to ensure that the grid achieves a dynamic optimal balance between economy and low carbon emissions under the premise of safety and stability.
[0016] A wind power generation system dispatching device that takes into account carbon emissions, comprising: The data acquisition unit collects wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data from the wind power plant as the initial dataset; The data preprocessing unit uses validity verification and bad data correction methods to preprocess the data in the initial dataset to obtain the target dataset; The model building unit constructs a day-ahead dispatch optimization model based on carbon emission costs to reduce carbon emissions and operating costs. Specifically, it constructs a mathematical model that comprehensively considers constraints such as generator power generation, fuel consumption, and carbon emission, with economic cost and carbon emission reduction targets as optimization objectives. The goal is to reduce operating costs and carbon emissions while meeting load demand. The objective function formula is as follows:
[0017] in, This represents the total number of scheduling periods; This represents the total number of generator sets. For scheduling periods; Number the generator set; Let be the unit power generation cost of the i-th generator set; Let be the power output of the i-th generator unit during time period t; Let be the unit carbon emission cost coefficient of the i-th generator set; Let be the carbon emissions of the i-th generator unit during time period t; Constraint and restriction units are used to construct a hierarchical constraint system and employ multiple types of constraints to limit power grid security. The optimal low-carbon dispatch scheme acquisition unit obtains the optimal low-carbon dispatch scheme based on real-time power grid operation data and carbon emission constraints.
[0018] A network-side server includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the carbon emission-based wind power generation system scheduling method.
[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the wind power generation system scheduling method taking into account carbon emissions.
[0020] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a wind power generation system scheduling method and related apparatus that takes carbon emissions into account. It collects wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data as initial datasets. The initial datasets are preprocessed using validity verification and bad data correction methods to obtain target datasets. A day-ahead scheduling optimization model considering carbon emissions is constructed based on carbon emission costs to reduce carbon emissions and operating costs. A hierarchical constraint system is constructed, using multiple types of constraints to limit power grid security. Based on real-time power grid operating data and carbon emission constraints, the optimal low-carbon scheduling scheme is obtained. By setting up a multi-source heterogeneous data fusion acquisition system, a day-ahead scheduling optimization model considering carbon emission costs, a hierarchical power grid security constraint system, and a rolling optimization feedback mechanism, the method achieves a synergistic optimization effect that balances economic efficiency and low-carbon development in the wind power generation system. Attached Figure Description
[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0022] Figure 1 This is a flowchart of a wind power generation system scheduling method that takes carbon emissions into account, according to the present invention.
[0023] Figure 2 This is a schematic diagram of the network-side server provided by the present invention.
[0024] Figure 3 This is a structural block diagram of a wind power generation system dispatching device that takes carbon emissions into account, according to the present invention. Detailed Implementation
[0025] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.
[0026] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0028] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0029] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.
[0030] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0031] Example 1 This invention provides a wind power generation system scheduling method that takes carbon emissions into account. It collects wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data as initial datasets. The initial datasets are preprocessed using validity verification and bad data correction methods to obtain target datasets. A day-ahead scheduling optimization model that takes carbon emissions into account is constructed based on carbon emission costs to reduce carbon emissions and operating costs. A hierarchical constraint system is constructed, using multiple types of constraints to limit power grid security. Based on real-time power grid operating data and carbon emission constraints, the optimal low-carbon scheduling scheme is obtained. By setting up a multi-source heterogeneous data fusion acquisition system, a day-ahead scheduling optimization model that takes carbon emission costs into account, a hierarchical power grid security constraint system, and a rolling optimization feedback mechanism, the method achieves a synergistic optimization effect that balances economic efficiency and low-carbon development in the wind power generation system.
[0032] The following is a detailed description of the implementation details of a wind power generation system dispatching method considering carbon emissions according to the present invention. The following content is only for ease of understanding and is not necessary for implementing this solution. See Figure 1 S1 collects wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data from wind power plants as the initial dataset.
[0033] Specifically, multi-source heterogeneous data fusion technology is adopted. During the data acquisition phase, multi-dimensional data is comprehensively collected through smart meters, meteorological monitoring equipment, generator set distributed control system and power grid monitoring and data acquisition system. This includes load forecasting, power generation forecasting, unit technical parameters and power grid structure data. At the same time, a carbon emission factor database is constructed to meet the data requirements of the scheduling method. The obtained data is used as the initial dataset.
[0034] Wind turbine operating parameters: Collect real-time active power, reactive power, blade angle, nacelle temperature, gearbox oil temperature, generator speed and other operating status parameters for each wind turbine, monitor the vibration data of the wind turbine, bearing temperature and other key component health indicators, and provide data support for wind turbine performance evaluation and fault early warning.
[0035] Meteorological forecast data: Acquire short-term and ultra-short-term forecast data of meteorological elements such as wind speed, wind direction, air pressure, temperature, humidity, and precipitation in the wind farm area, and combine them with historical meteorological data and wind turbine power characteristic curves to improve the accuracy of wind power prediction.
[0036] Power grid operation status data: Monitor real-time data such as voltage at each node of the power grid, line power flow, frequency fluctuations, and reactive power distribution, analyze the stability and security of power grid operation, ensure that the power grid can still meet the constraints of safe and stable operation after the wind power generation system is connected, and provide data support for the system's reactive power optimization scheduling.
[0037] Carbon trading market data: Collect information such as carbon allowance prices, carbon trading volume, changes in carbon trading policies, and supply and demand in the carbon market. Combine this with system carbon emission data and optimize dispatch strategies through economic means to reduce the carbon emission costs of system operation while meeting power supply demand, thereby improving the economic and environmental benefits of wind power generation systems.
[0038] S2, the data in the initial dataset is preprocessed using methods of validity verification and bad data correction to obtain the target dataset; Step S21: Use the validity verification method to filter out outliers in the initial dataset.
[0039] Based on the physical laws of the power grid, constraints are set for data filtering, specifically for unit output data. It must meet its minimum output. and maximum output The limitation, namely For load data L, compare it with historical load data for the same period. In comparison, it needs to meet the following requirements. ,in The set reasonable deviation coefficient is used to trigger the abnormal data identification mechanism when the unit output data and load data exceed the set value, and the data in question is removed.
[0040] Step S22: Process the abnormal data using a bad data correction method.
[0041] During power system dispatching, measurement data is easily affected by factors such as communication interference and equipment failure, resulting in outliers. The presence of abnormal data can seriously affect the accuracy and reliability of the dispatching model. A weighted average interpolation method is used to process outliers. The specific formula for the weighted average interpolation method is as follows:
[0042] in, This represents the new value of the i-th position after calculation and update; Indicates the i-th The weight of a single position; Indicates the i-th The original value at one position; The weights are for the (i+1)th position; These are the original values at position i+1; By using validity verification to remove outliers, and then using weighted average interpolation, the data change trend is accurately reflected, reducing the interference of outlier data on subsequent power system dispatch analysis, and thus obtaining the target dataset.
[0043] S3 is a day-ahead scheduling optimization model that takes carbon emissions into account, based on carbon emission costs, to reduce carbon emissions and operating costs. Specifically, a mathematical model is constructed that comprehensively considers constraints such as generator power output, fuel consumption, and carbon emissions. With economic cost and carbon reduction targets as optimization objectives, the power generation plan for the next 24 hours or multiple time periods is optimized. Under the premise of meeting load demand, the power system reduces operating costs and carbon emissions, balancing economic benefits with environmental protection. The objective function formula is as follows:
[0044] in, This represents the total number of scheduling periods; This represents the total number of generator sets. For scheduling periods; Number the generator set; Let be the unit power generation cost of the i-th generator set; Let be the power output of the i-th generator unit during time period t; Let be the unit carbon emission cost coefficient of the i-th generator set; Let be the carbon emissions of the i-th generator unit during time period t; The constraints are power balance constraints, upper and lower limits of power generation constraints, and carbon emission constraints. Relevant data are added to the objective function to achieve parallel and coordinated optimization of economic and low-carbon development. Power balance constraints:
[0045] in, for System load demand during a given time period; Upper and lower limits of power generation constraints:
[0046] in, Let be the minimum generating power of the i-th generator set; Let be the maximum generating power of the i-th generator set; Carbon emission constraints:
[0047] in, This represents the maximum allowable carbon emissions during time period t. Step S31: Based on the current carbon emission quotas in the carbon trading market, quantify the relationship between the carbon emissions of the power system and the carbon quotas.
[0048] Specifically, The total carbon emission allowance for the system; For the actual carbon emissions of the system, when If this happens, the system will need to purchase excess quotas, incurring additional costs. If the current quota is not used up, the quota can be sold to generate revenue. Step S32: Calculate the carbon cost of thermal power generation based on the current carbon emissions.
[0049] Specifically, the carbon cost calculation formula is as follows:
[0050] in, Carbon tax per unit This represents the total number of thermal power units in the system. Let be the carbon emission intensity of the i-th thermal power unit; Let be the power generation of the i-th thermal power unit during time period t; The duration of the scheduling period; Based on carbon costs and carbon market trading mechanisms, carbon emissions can be reduced and costs saved while ensuring power generation.
[0051] S4. Construct a hierarchical constraint system and use multiple types of constraints to limit power grid security; Specifically, starting from the core requirements of power system operation, a hierarchical constraint system is constructed to constrain power balance, unit operation, and wind power consumption, thereby avoiding conflicts with the optimal operation scheme calculated by the objective function.
[0052] Step S41: Based on the principle of instantaneous power balance in the power system, a dynamic balance equation is constructed between the generation side and the load side. Power balance constraints are used to force a real-time balance between the power generation of conventional units, wind power output, energy storage charging and discharging power, and system load and grid losses, ensuring that the system does not experience a power deficit during the dispatch cycle and maintains frequency stability and voltage level. The power balance constraint formula is as follows:
[0053] in, The number of thermal power generating units; Let be the active power output of the i-th thermal power generating unit at time t; This represents the active power output of the wind turbine generator at time t. The predicted load power of the system at time t; The set of all moments within the scheduling period; The power balance equation holds true throughout the scheduling period.
[0054] Step S42, the unit operation constraints include the upper and lower limits of unit output and the ramp rate; The upper and lower limits of output are set according to the unit's technical parameters. These limits constrain the unit's output to stay within the technically permissible range, preventing overload or underload operation. The formulas for the upper and lower limits of output are as follows:
[0055] in, Let i be the operating state variable of device i at time t; The minimum active power output of device i; The actual active power output of device i at time t; The maximum active power output of device i; This holds true for all devices i and all times t.
[0056] The ramp rate constraint limits the rate of change of unit power to prevent damage to unit equipment due to sudden power changes; the ramp rate constraint formula is as follows:
[0057] Among them, the active power output of the i-th unit at time t. Let be the active power output of the i-th generating unit at time t-1; The maximum load increase rate of the i-th unit; For time intervals; Step S43: To address the intermittency and volatility of wind power output, the wind power priority consumption strategy is optimized by setting a confidence interval for predicted wind power output. The wind farm operation constraints limit the planned wind power output range. The wind farm operation constraint formula is as follows:
[0058] in, The active power output of the wind turbine generator at time t; The maximum active power output of the wind turbine generator at time t; This is the lower limit of power output; S5, based on real-time power grid operation data and carbon emission constraints, yields the optimal low-carbon dispatch scheme.
[0059] Specifically, the system continuously integrates real-time grid operation data and ultra-short-term wind power forecasts to detect deviations between system status and plans. Subsequently, based on the day-ahead plan, a rapid rolling optimization model is initiated to dynamically solve the optimal power generation adjustment scheme in the shortest future time period under the constraints of carbon emissions and grid security. Finally, the automatic control system executes dispatch instructions and forms a real-time closed loop to ensure that the grid achieves a dynamic optimal balance between economy and low carbon emissions under the premise of safety and stability.
[0060] Step S51: Based on the current system optimization scheme, construct a rolling optimization model to further adjust the optimization objectives.
[0061] Specifically, a rolling optimization is initiated with a fixed short cycle, with the optimization window covering the next few hours, but only the decision of the first period is executed. Through carbon emission optimization in S3 and constraints in S4, production costs are adjusted to obtain the optimal power adjustment command for each unit, the planned output adjustment value of the wind farm, and the switching strategy of reactive power compensation equipment in the shortest future period.
[0062] Step S52: Execute the adjustment according to the required optimal scheme to obtain the optimal scheduling scheme for the wind power generation system.
[0063] Specifically, the optimal wind power generation system scheduling scheme is obtained based on the rolling optimization model. The instructions are automatically sent to the centralized control system and reactive power compensation device of each power plant and wind farm through the energy management system. The controlled equipment receives and executes the instructions. The actual operating status data after execution is collected and captured by S1 again to form a closed-loop feedback, thus obtaining the optimal wind power generation system scheduling scheme that takes into account both economy and low carbon emissions.
[0064] The initial dataset was collected from wind turbine operating parameters, meteorological forecasts, power grid operating status data, and carbon trading market data. The data in the initial dataset was preprocessed using validity verification and bad data correction methods to obtain the target dataset. A day-ahead dispatch optimization model incorporating carbon emission costs was constructed to reduce carbon emissions and operating costs. A hierarchical constraint system was established, employing multiple constraint methods to limit power grid security. Based on real-time power grid operating data and carbon emission constraints, the optimal low-carbon dispatch scheme was obtained. By setting up a multi-source heterogeneous data fusion acquisition system, a day-ahead dispatch optimization model incorporating carbon emission costs, a hierarchical power grid security constraint system, and a rolling optimization feedback mechanism, the wind power generation system achieved a synergistic optimization effect that balances economic efficiency and low carbon emissions. Under the premise of ensuring the safe and stable operation of the power grid, the system's carbon emissions and operating costs were reduced, while improving wind power absorption capacity and dispatch accuracy.
[0065] Example 2 like Figure 3 As shown, the present invention provides a wind power generation system dispatching device that takes carbon emissions into account, comprising: The data acquisition unit collects wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data from the wind power plant as the initial dataset; The data preprocessing unit uses validity verification and bad data correction methods to preprocess the data in the initial dataset to obtain the target dataset; The model building unit constructs a day-ahead dispatch optimization model based on carbon emission costs to reduce carbon emissions and operating costs. Specifically, it constructs a mathematical model that comprehensively considers constraints such as generator power generation, fuel consumption, and carbon emission, with economic cost and carbon emission reduction targets as optimization objectives. The goal is to reduce operating costs and carbon emissions while meeting load demand. The objective function formula is as follows:
[0066] in, This represents the total number of scheduling periods; This represents the total number of generator sets. For scheduling periods; Number the generator sets; Let be the unit power generation cost of the i-th generator set; Let be the power output of the i-th generator unit during time period t; Let be the unit carbon emission cost coefficient of the i-th generator set; Let be the carbon emissions of the i-th generator unit during time period t; Constraint and restriction units are used to construct a hierarchical constraint system and employ multiple types of constraints to limit power grid security. The optimal low-carbon dispatch scheme acquisition unit obtains the optimal low-carbon dispatch scheme based on real-time power grid operation data and carbon emission constraints.
[0067] Example 3 like Figure 2 As shown, the present invention provides a network-side server, including at least one processor 302; and a memory 301 communicatively connected to at least one processor 302; wherein the memory 301 stores instructions executable by at least one processor 302, the instructions being executed by at least one processor 302 to enable at least one processor 302 to perform the steps of the above-described data processing method.
[0068] The memory 301 and processor 302 are connected via a bus, which may include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 302 and memory 301 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 302 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 302.
[0069] Processor 302 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 301 can be used to store data used by processor 302 during operation.
[0070] Example 4 This invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of a wind power generation system scheduling method that takes carbon emissions into account.
[0071] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0072] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.
[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0075] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
[0076] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.
Claims
1. A method for dispatching a wind power generation system that takes carbon emissions into account, characterized in that, include: S1, collect wind turbine operating parameters, weather forecast data, power grid operation status data and carbon trading market data from wind power plants as the initial dataset; S2, the data in the initial dataset is preprocessed using methods of validity verification and bad data correction to obtain the target dataset; S3, based on carbon emission costs, constructs a day-ahead dispatch optimization model that considers carbon emissions to reduce carbon emissions and operating costs. Specifically, a mathematical model is constructed that comprehensively considers the constraints of generator power generation, fuel consumption, and carbon emission, with economic cost and carbon emission reduction targets as optimization objectives. Under the premise of meeting load demand, the power system reduces operating costs and carbon emissions. The objective function formula is as follows: in, This represents the total number of scheduling periods; This represents the total number of generator sets. For scheduling periods; Number the generator set; Let be the unit power generation cost of the i-th generator set; Let be the power output of the i-th generator unit during time period t; Let be the unit carbon emission cost coefficient of the i-th generator set; Let be the carbon emissions of the i-th generator unit during time period t; S4. Construct a hierarchical constraint system and use multiple types of constraints to limit power grid security; S5, based on real-time power grid operation data and carbon emission constraints, yields the optimal low-carbon dispatch scheme.
2. The wind power generation system dispatching method considering carbon emissions according to claim 1, characterized in that, In S1, the initial dataset is collected from wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data. This includes: using multi-source heterogeneous data fusion technology, during the data acquisition phase, multi-dimensional data is comprehensively collected through smart meters, weather monitoring equipment, generator set distributed control systems, and power grid monitoring and data acquisition systems. This data covers load forecasting, power generation forecasting, generator set technical parameters, and power grid structure data. At the same time, a carbon emission factor database is constructed to meet the data requirements of the scheduling method. The obtained data is used as the initial dataset.
3. The wind power generation system dispatching method considering carbon emissions according to claim 2, characterized in that, In S2, the data in the initial dataset is preprocessed using validity verification and bad data correction methods to obtain the target dataset, which includes: Step S21: Use a validity verification method to filter out outliers in the initial dataset; Based on the physical laws of the power grid, constraints are set for data filtering, specifically for unit output data. It must meet its minimum output. and maximum output The limitation, namely For load data L, compare it with historical load data for the same period. In comparison, it needs to meet the following requirements. ,in The set reasonable deviation coefficient is used to trigger the abnormal data identification mechanism when the unit output data and load data exceed the set value, and the data in question is removed.
4. A wind power generation system dispatching method considering carbon emissions according to claim 3, characterized in that, Step S22: Process the abnormal data using a bad data correction method; During power system dispatching, measurement data is easily affected by communication interference and equipment failures, resulting in outliers. The presence of abnormal data can seriously affect the accuracy and reliability of the dispatching model. A weighted average interpolation method is used to process outliers. The specific formula for the weighted average interpolation method is as follows: in, This represents the new value of the i-th position after calculation and update; Indicates the i-th The weight of a single position; Indicates the i-th The original value at one position; The weights are for the (i+1)th position; These are the original values at position i+1.
5. A wind power generation system dispatching method considering carbon emissions according to claim 1, characterized in that, In S4, a hierarchical constraint system is constructed, and multiple types of constraints are used to limit grid security, including: Step S41, based on the principle of instantaneous power balance of the power system, a dynamic balance equation is constructed between the generation side and the load side, and power balance constraints are used to force conventional unit power generation, wind power output, energy storage charging and discharging power to maintain real-time balance with system load and grid losses. The power balance constraint formula is as follows: in, The number of thermal power generating units; Let be the active power output of the i-th thermal power generating unit at time t; This represents the active power output of the wind turbine generator at time t. The predicted load power of the system at time t; The set of all moments within the scheduling period; The power balance equation holds true throughout the scheduling period.
6. A wind power generation system dispatching method considering carbon emissions according to claim 6, characterized in that, Step S42, the unit operation constraints include the upper and lower limits of unit output and the ramp rate; The upper and lower limits of output are set according to the unit's technical parameters. These limits constrain the unit's output to stay within the technically permissible range, preventing overload or underload operation. The formulas for the upper and lower limits of output are as follows: in, Let i be the operating state variable of device i at time t; The minimum active power output of device i; The actual active power output of device i at time t; The maximum active power output of device i; This holds true for all devices i and all times t.
7. A wind power generation system dispatching method considering carbon emissions according to claim 1, characterized in that, In S5, based on real-time grid operation data and carbon emission constraints, the optimal low-carbon dispatch scheme is obtained as follows: the system continuously integrates real-time grid operation data and ultra-short-term wind power forecasts to sense system status and plan deviations; subsequently, based on the day-ahead plan, a fast rolling optimization model is launched to dynamically solve the optimal power generation adjustment scheme in the shortest future time period under the constraints of carbon emissions and grid security; finally, the dispatch instructions are executed through the automatic control system to form a real-time closed loop, ensuring that the grid achieves a dynamic optimal balance between economy and low carbon emissions under the premise of safety and stability.
8. A wind power generation system dispatching device that takes carbon emissions into account, characterized in that, include: The data acquisition unit collects wind turbine operating parameters, weather forecast data, power grid operating status data, and carbon trading market data from the wind power plant as the initial dataset; The data preprocessing unit uses validity verification and bad data correction methods to preprocess the data in the initial dataset to obtain the target dataset; The model building unit constructs a day-ahead dispatch optimization model based on carbon emission costs to reduce carbon emissions and operating costs. Specifically, it constructs a mathematical model that comprehensively considers constraints such as generator power generation, fuel consumption, and carbon emission, with economic cost and carbon emission reduction targets as optimization objectives. The goal is to reduce operating costs and carbon emissions while meeting load demand. The objective function formula is as follows: in, This represents the total number of scheduling periods; This represents the total number of generator sets. For scheduling periods; Number the generator set; Let be the unit power generation cost of the i-th generator set; Let be the power output of the i-th generator unit during time period t; Let be the unit carbon emission cost coefficient of the i-th generator set; Let be the carbon emissions of the i-th generator unit during time period t; Constraint and restriction units are used to construct a hierarchical constraint system and employ multiple types of constraints to limit power grid security. The optimal low-carbon dispatch scheme acquisition unit obtains the optimal low-carbon dispatch scheme based on real-time power grid operation data and carbon emission constraints.
9. A network-side server, characterized in that, include: At least one processor; The system includes a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the wind power generation system scheduling method taking into account carbon emissions as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the wind power generation system scheduling method taking into account carbon emissions as described in any one of claims 1 to 7.