New energy station dynamic power control method and system based on energy storage system

Abstract

The invention relates to the technical field of new energy, in particular to a new energy station dynamic power control method and system based on an energy storage system, and the method comprises the steps: dividing the energy storage system into a quick response unit, a medium-speed adjustment unit and a slow migration unit; based on the intraday power gap curve and the state information of the energy storage system, determining a target output power curve of each intraday scheduling period of a slow migration unit and a medium-speed adjustment unit in the energy storage system; controlling power output of the slow migration unit and the medium-speed regulation unit according to a target output power curve; and monitoring fluctuation data of new energy output in real time, and triggering a quick response unit in the energy storage system to execute power compensation according to the fluctuation data. According to the invention, the energy storage system is divided into the quick response unit, the medium-speed regulation unit and the slow migration unit which are respectively used for power regulation of different time scales, so that cooperative control of multiple time scales is realized, and the response efficiency and the resource utilization rate of the energy storage system are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a dynamic power control method and system for new energy power plants based on energy storage systems. Background Technology

[0002] With the rapid growth of new energy installed capacity, renewable energy sources such as photovoltaics and wind power are accounting for an increasing proportion of the power system. However, due to their significant susceptibility to weather conditions, their output power is highly volatile and intermittent, making it difficult to stably participate in grid dispatch, which poses a severe challenge to the system's frequency stability and peak-shaving and frequency regulation capabilities.

[0003] To address the operational risks to the power system caused by fluctuations in renewable energy output, energy storage systems, as a flexible regulatory resource, are widely used in renewable energy power plants. By rationally scheduling the charging and discharging behavior of energy storage systems, smooth processing of renewable energy output, frequency support, and energy migration can be achieved. However, most existing energy storage scheduling strategies treat the energy storage system as a whole for unified control, failing to fully consider the differences in response speed, regulation capability, and applicable scenarios among different energy storage units. This results in difficulties in optimizing the utilization efficiency of energy storage resources and the system response effect.

[0004] In addition, existing methods mostly adopt static strategies when scheduling energy storage, making it difficult to combine prior information such as weather forecasts and load forecasts to proactively regulate future new energy fluctuation trends. They also lack dynamic response mechanisms to rapid disturbances, making it difficult to simultaneously take into account intraday energy optimization and real-time frequency control. Summary of the Invention

[0005] (a) Purpose of the invention The purpose of this invention is to provide a dynamic power control method and system for new energy power plants based on energy storage systems. This method divides the energy storage system into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which are used for power regulation at different time scales, thereby achieving coordinated control across multiple time scales and improving the response efficiency and resource utilization of the energy storage system.

[0006] (II) Technical Solution To address the above problems, this invention provides a dynamic power control method for new energy power plants based on energy storage systems, comprising: The energy storage system is divided into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which are used for short-time frequency support, minute-level power smoothing, and hour-level energy migration, respectively. Based on the intraday meteorological forecast information, the predicted new energy output curve is obtained. The intraday meteorological forecast information includes light intensity forecast data and wind speed forecast data. By comparing the daily load forecast data with the new energy source forecast output curve, the daily power gap curve is obtained. Based on the intraday power deficit curve and the state information of the energy storage system, the target output power curves of the slow migration unit and the medium speed regulation unit in the energy storage system for each intraday scheduling cycle are determined; the power output of the slow migration unit and the medium speed regulation unit is controlled according to the target output power curves. Real-time monitoring of fluctuations in new energy output data triggers a rapid response unit in the energy storage system to perform power compensation based on the fluctuation data.

[0007] In another aspect of the present invention, preferably, the division of the energy storage system into a fast response unit, a medium-speed regulation unit, and a slow migration unit includes: Energy storage systems are divided into fast response units, medium-speed regulation units, and slow migration units according to their response speed and capacity. The fast response unit uses an energy storage medium with a response time in the millisecond range, and the capacity ratio of the fast response unit is less than or equal to a first capacity ratio of the total installed capacity of the new energy power station. The medium-speed regulation unit uses an energy storage medium with a response time in the range of seconds to minutes, and the capacity ratio of the medium-speed regulation unit is between a first capacity ratio and a second capacity ratio. The slow migration unit uses an energy storage medium with a response time in the range of minutes to hours, and the capacity ratio of the slow migration unit is greater than or equal to the second capacity ratio.

[0008] In another aspect of the present invention, preferably, the energy storage medium of the fast response unit is a supercapacitor, a lithium-titanium battery, or other energy storage medium with high power density; the energy storage medium of the medium speed regulation unit is a lithium iron phosphate battery, a ternary lithium battery, or a flow battery; and the energy storage medium of the slow migration unit is a flow battery, compressed air energy storage, or a large lithium battery pack.

[0009] In another aspect of the present invention, preferably, obtaining the predicted new energy output curve based on intraday meteorological forecast information includes: The light intensity prediction data is input into a preset photovoltaic power prediction model, which is based on a neural network model, to obtain a photovoltaic output prediction curve. The wind speed prediction data is input into the wind power prediction model, which is based on a deep learning model, to obtain the wind power output prediction curve. The predicted power output curves of photovoltaic power and wind power are superimposed to obtain the predicted new energy power output curves.

[0010] In another aspect of the present invention, preferably, determining the target output power curves of the slow migration unit and the medium-speed regulation unit in the energy storage system for each scheduling cycle during the day based on the intraday power gap curve and the state information of the energy storage system includes: The intraday power gap curve is decomposed to extract the slow-changing component containing hourly trend changes and the fast-changing component containing minute-level fluctuations. Acquire the status information of the slow migration unit and the medium speed regulation unit in the energy storage system. The status information includes their respective state of charge, available capacity and maximum charge and discharge power. Based on the slow-changing component, the fast-changing component, and the state information, the power regulation task in each scheduling cycle is allocated, and the target output power curves of the slow migration unit and the medium-speed regulation unit in each scheduling cycle are generated respectively.

[0011] In another aspect of the present invention, preferably, the intraday power gap curve is decomposed to extract a slow-changing component containing hourly-level trend changes and a fast-changing component containing minute-level fluctuations, including: The intraday power gap curve is discretely sampled according to a fixed time step to form a discrete time series. The time series is subjected to moving average filtering, with the moving window width set to 1 hour, to obtain the slow-varying components representing hourly trend changes; By subtracting the slow-varying component from the discrete time series, the remaining part is obtained, which is the fast-varying component representing minute-level fluctuations.

[0012] In another aspect of the present invention, preferably, the acquisition of the state information of the slow migration unit and the medium-speed regulation unit in the energy storage system, the state information including their respective state of charge, available capacity, and maximum charge / discharge power, includes: Based on the state of charge, available capacity, and maximum charge / discharge power of the slow migration unit, determine the first output power range allowed for the current scheduling cycle of the slow migration unit; Based on the state of charge, available capacity, and maximum charge / discharge power of the medium-speed regulating unit, the second output power range allowed for the current scheduling cycle of the medium-speed regulating unit is determined.

[0013] In another aspect of the present invention, preferably, based on the slow-changing component, the fast-changing component, and the state information, the power regulation task within each scheduling cycle is allocated, and the target output power curves of the slow migration unit and the medium-speed regulation unit in each scheduling cycle are generated respectively, including: Within the first output power range and the second output power range, the fast-change component is allocated to the medium-speed regulation unit; The allocation ratio of the slow-varying component to the slow-speed migration unit and the medium-speed adjustment unit is calculated. The calculation of the allocation ratio is based on the fact that while meeting the adjustment requirements of the slow-varying component and the fast-varying component, the target output power of the slow-varying unit changes the least with respect to the output power of the previous scheduling cycle. The target output power curve of the slow migration unit in each scheduling cycle is generated based on the slow variable component and the allocation ratio, and the target output power curve of the medium speed regulation unit in each scheduling cycle is generated based on the fast variable component, the slow variable component and the allocation ratio.

[0014] In another aspect of the present invention, preferably, real-time monitoring of fluctuation data in the output of new energy sources, and triggering a fast response unit in the energy storage system to perform power compensation based on the fluctuation data, includes: Based on the fluctuation data and the target output power within the current scheduling period, the power deviation value for the current scheduling period is obtained; Determine whether the power deviation value exceeds the set steady-state disturbance threshold; If the steady-state disturbance threshold is exceeded, the fast response unit is triggered using the state information of the fast response unit as a constraint, and the charging or discharging power is output in reverse according to the deviation value.

[0015] In another aspect, preferably, a dynamic power control system for new energy power plants based on an energy storage system includes: Module division: The energy storage system is divided into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which are used for short-time frequency support, minute-level power smoothing, and hour-level energy migration, respectively. New energy output module: Based on the intraday meteorological forecast information, obtain the predicted new energy output curve, wherein the intraday meteorological forecast information includes light intensity forecast data and wind speed forecast data; The acquisition module compares the daily load forecast data with the new energy forecast output curve to obtain the daily power gap curve. Determination Module: Based on the intraday power gap curve and the state information of the energy storage system, determine the target output power curves of the slow migration unit and the medium speed regulation unit in the energy storage system for each intraday scheduling cycle; control the power output of the slow migration unit and the medium speed regulation unit according to the target output power curves; Triggering module: Monitors the fluctuation data of new energy output in real time, and triggers the fast response unit in the energy storage system to perform power compensation based on the fluctuation data.

[0016] (III) Beneficial Effects The above-described technical solution of the present invention has the following beneficial technical effects: This invention divides the energy storage system into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which respectively undertake power regulation tasks at the millisecond, minute, and hourly levels, achieving coordinated control across multiple time scales and significantly improving system response speed and regulation accuracy. By incorporating intraday meteorological forecasts and load forecast data, new energy output forecast curves and power gap curves are established, enabling advance planning of the target power curve for the energy storage system and improving the foresight of energy storage dispatch and the capacity for new energy absorption. During operation, real-time monitoring of new energy output fluctuations automatically triggers the fast response unit for power compensation, and adaptively adjusts the response power according to the frequency and amplitude of disturbances, effectively suppressing the impact of short-term fluctuations on the grid frequency and ensuring stable system operation. Attached Figure Description

[0017] Figure 1 This is an overall flowchart of one embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0019] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0021] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0022] The invention will now be described in more detail with reference to the accompanying drawings. In the various drawings, the same elements are indicated by similar reference numerals. For clarity, the various parts in the drawings are not drawn to scale.

[0023] Example 1 A dynamic power control method for new energy power plants based on energy storage systems. Figure 1 An overall flowchart of one embodiment of the present invention is shown, as follows: Figure 1 As shown, it includes: The energy storage system is divided into a fast response unit, a medium-speed regulation unit, and a slow migration unit, used for short-term frequency support, minute-level power smoothing, and hour-level energy migration, respectively. In this embodiment, the division criteria are response speed and capacity. The fast response unit is mainly used for millisecond-level rapid power compensation for high-frequency disturbances in renewable energy output, adapting to scenarios such as frequency support or inertia virtualization. The energy storage medium of the fast response unit is a supercapacitor, lithium-titanium battery, or other energy storage medium with high power density, such as supercapacitors and lithium-titanium batteries. To avoid wasting overall system resources, the capacity ratio of this unit is generally designed to not exceed the first proportion of the total installed capacity of the renewable energy power station, for example, 5% to 10%, to ensure that it has instantaneous high power output capability while controlling costs.

[0024] The medium-speed regulation unit buffers and compensates for short-to-medium timescale fluctuations in new energy sources, primarily used for smooth power regulation within minutes. Its response time is typically between several seconds and minutes, exhibiting good energy density and cycle life. The energy storage medium of the medium-speed regulation unit is a lithium iron phosphate battery, a ternary lithium battery, or a flow battery; its capacity is designed to be between a first capacity ratio and a second capacity ratio, for example, 10% to 30%, to ensure good buffering capacity against moderate disturbances without affecting slow energy regulation.

[0025] Slow-migration units are primarily used for hourly energy balance regulation tasks, such as peak shaving and valley filling, intraday scheduling, and wind and solar curtailment control. They require large energy capacity and long continuous discharge capability, with response times typically ranging from minutes to hours. The energy storage medium for these slow-migration units is flow batteries, compressed air energy storage, or large lithium battery packs. The capacity of this unit typically accounts for a greater than or equal to the second capacity ratio, for example, ≥30%, to support long-term compensation needs for intraday fluctuations in renewable energy.

[0026] The types and capacities of energy storage units are optimized based on the scale of the power plant, load characteristics, regional power grid regulation capacity, and economic analysis, offering good flexibility and engineering feasibility. Through hierarchical design, regulation tasks at different time scales are rationally allocated, and each energy storage unit plays an efficient role in its preferred time domain, thereby achieving dynamic stability of power output and high-quality grid-connected operation of new energy power plants.

[0027] Based on intraday meteorological forecast information, a predicted renewable energy output curve is obtained. The intraday meteorological forecast information includes predicted solar irradiance and wind speed data. Here, "intraday" refers to 24 hours. The process involves inputting the predicted solar irradiance data into a preset photovoltaic power prediction model. This model is based on a neural network model to obtain the photovoltaic output prediction curve. The photovoltaic power prediction model is constructed based on an artificial neural network structure, which can be a feedforward multilayer perceptron or a long short-term memory network. Supervised learning training is performed using a large amount of historical power data and meteorological observation data to output the photovoltaic power output for the predicted period, forming the photovoltaic output prediction curve.

[0028] The wind speed prediction data is input into a wind power prediction model, which is based on a deep learning model to obtain a wind power output prediction curve. The wind power prediction model employs a deep learning-based time series prediction method, which can be a combination of convolutional neural networks and gated recurrent units or Transformer structures. It can learn the nonlinear relationship between wind speed changes and power output, and has strong generalization ability in scenarios with multiple wind turbines and multiple measurement points, outputting a wind power output prediction curve.

[0029] The predicted power output curves of photovoltaic (PV) and wind power (WH) are overlaid to obtain the predicted new energy power output curve. These curves are then aligned by time and overlaid to form a unified new energy power output prediction curve, which serves as the basis for subsequent power gap analysis and energy storage dispatch calculations.

[0030] The daily load forecast data is compared with the predicted output curve of new energy sources to obtain the daily power gap curve. The load forecast data can be used to achieve short-term load forecasting through models such as support vector machines, multilayer perceptrons, and LSTM. The forecast time range covers multiple scheduling cycles within the day, with a time granularity of 5 minutes, 15 minutes, or 1 hour. The output is a continuous load forecast time series. For each scheduling cycle, the predicted output of new energy sources is differiated from the predicted load value to obtain the power gap value. The power difference for all scheduling cycles is calculated sequentially to construct a power gap curve covering the entire day.

[0031] Based on the intraday power deficit curve and the state information of the energy storage system, the target output power curves of the slow migration unit and the medium-speed regulation unit in the energy storage system are determined for each intraday scheduling cycle, including: The intraday power gap curve is decomposed to extract a slow-changing component containing hourly trend changes and a fast-changing component containing minute-level fluctuations, including: The intraday power gap curve is discretely sampled at a fixed time step to form a discrete time series; the intraday power gap curve is sampled at equal intervals at a fixed time step, such as 5 minutes, to obtain a set of discrete time series, which is used for subsequent time-frequency decomposition processing.

[0032] The time series is subjected to moving average filtering with a sliding window width of 1 hour to obtain the slow-varying component representing the hourly trend change. The sliding window width of 1 hour includes multiple sampling points with fixed time steps, such as 12 5-minute sampling points. For each time point, the average value of the time before and after the point is calculated as the slow-varying component at that moment. Through this method, a new time series is obtained, reflecting the trend change of the power gap on an hourly scale.

[0033] By subtracting the slowly varying component from the discrete-time series, the remaining portion is obtained, which represents the fast varying component representing minute-level fluctuations. The fast varying component is obtained by subtracting the original power gap series from the aforementioned slowly varying component series.

[0034] The system acquires the status information of the slow migration unit and the medium-speed regulation unit in the energy storage system. This status information includes their respective states of charge (SOC), available capacity, and maximum charge / discharge power. Specifically, based on the SOC, available capacity, and maximum charge / discharge power of the slow migration unit, a first allowable output power range for the current scheduling cycle is determined. Similarly, based on the SOC, available capacity, and maximum charge / discharge power of the medium-speed regulation unit, a second allowable output power range for the current scheduling cycle is determined. Specifically, before the start of each scheduling cycle, the current operating status of each energy storage unit is collected in real time, including: the current SOC value, reflecting the proportion of remaining battery capacity; available capacity, referring to the currently available charge / discharge capacity; and the maximum allowable charging and discharging power of the device. After acquiring the above status information, the allowable power output range within the current scheduling cycle is calculated based on the duration of the current scheduling cycle, combined with available capacity and power limitations. For slow-migrating units, based on their current SOC and available capacity, their maximum discharge power is determined to be no more than the available capacity divided by the scheduling cycle length, and their maximum charge power is determined to be no more than the remaining charge capacity divided by the scheduling cycle length, while also being limited by the unit's rated maximum charge / discharge power. Considering these factors, a first permissible output power range for the slow-migrating unit within the current scheduling cycle is determined. For medium-speed regulation units, the same method is used to calculate their second output power range within the current scheduling cycle based on their SOC, available capacity, and maximum charge / discharge power. These calculations ensure that subsequent power regulation tasks are allocated within the feasible range of the energy storage units, thereby achieving the safety, stability, and effectiveness of the energy storage system operation.

[0035] Specifically, the first output power range and the second output power range are calculated using the following formula: ; in, and This indicates the upper and lower limits of the output power range, and 'i' represents the number of the slow-speed migration unit and the medium-speed adjustment unit. Indicates the maximum discharge power. Indicates the maximum charging power. Indicates total energy capacity. Indicates the state of charge. and Represents the minimum and maximum energy boundaries under safe operation constraints. Indicates the duration of the scheduling cycle.

[0036] Based on the slow-changing component, fast-changing component, and state information, power regulation tasks are allocated for each scheduling cycle, and target output power curves for the slow migration unit and the medium-speed regulation unit are generated for each scheduling cycle, including: Within the first and second output power ranges, the fast-changing components are allocated to the medium-speed regulation unit; fast-changing components representing minute-level power fluctuations are preferentially allocated to the medium-speed regulation unit. Because the medium-speed regulation unit has a faster response speed and is suitable for handling rapidly fluctuating power regulation tasks, within its available power range, all fast-changing components within the current scheduling cycle are directly allocated to that unit for execution. If the medium-speed regulation unit cannot fully handle the fast-changing components in certain cycles, power reduction, partial transfer to other units, or temporary skipping strategies can be adopted to ensure the safety of system operation.

[0037] The allocation ratio of the slow-varying component to the slow-migration unit and the medium-speed regulation unit is calculated. This allocation ratio is based on minimizing the change in the target output power of the slow-migration unit compared to the output power of the previous scheduling cycle while meeting the regulation requirements of both slow and fast-varying components. For slow-varying components with hourly trend changes, a reasonable allocation ratio between the slow-migration unit and the medium-speed regulation unit is calculated based on the states of the two energy storage units in the current scheduling cycle. The principle for determining the allocation ratio is to minimize the change in the target output power of the slow-migration unit between the current scheduling cycle and the previous scheduling cycle, while meeting the overall power regulation requirements. This mitigates output fluctuations in the unit, extends its service life, and improves operational stability.

[0038] The target output power curves of the slow migration unit in each scheduling cycle are generated based on the slow-varying component and the allocation ratio. Similarly, the target output power curves of the medium-speed regulation unit in each scheduling cycle are generated based on the fast-varying component, the slow-varying component, and the allocation ratio. After the allocation ratio is determined, the system generates the target output power of each energy storage unit in the current scheduling cycle. Specifically, the target output power of the slow migration unit consists of the slow-varying component it undertakes; the target output power of the medium-speed regulation unit consists of two parts: the fast-varying component it undertakes and the slow-varying component it is allocated. By repeating the above allocation process for each scheduling cycle, complete target output power curves for the two energy storage units corresponding to each scheduling cycle throughout the day can be formed.

[0039] Real-time monitoring of renewable energy output fluctuations is achieved by comparing the predicted renewable energy output curve with the real-time monitored renewable energy output. Based on this fluctuation data, a fast response unit in the energy storage system is triggered to perform power compensation. By monitoring the fluctuations in renewable energy output in real time, the deviation between renewable energy output and system power demand within the scheduling cycle is promptly identified, and the fast response unit in the energy storage system is dynamically triggered to perform charge and discharge regulation tasks to quickly compensate for power disturbances caused by renewable energy fluctuations, thereby improving the stability of system output power and grid operation. This includes: Based on the fluctuation data and the target output power within the current scheduling period, the power deviation value for the current scheduling period is obtained. The collected real-time renewable energy output value is compared with the target output power value set according to the plan within the current scheduling period to calculate the power deviation value at the current moment. The deviation value is used to measure the difference between the actual renewable energy output value and the expected scheduling value. The system determines whether the power deviation exceeds a set steady-state disturbance threshold. This threshold distinguishes between normal, minor fluctuations and significant disturbances requiring proactive intervention. The steady-state disturbance threshold can be set based on grid dispatch requirements, system operating experience, or historical fluctuation levels, and is a symmetrical positive and negative range. If the power deviation is within the steady-state disturbance threshold range, the current fluctuation is within the system's acceptable range, and no proactive intervention is needed. If the power deviation exceeds the set threshold, it indicates that the new energy fluctuation has affected the power balance, and a rapid response mechanism needs to be triggered.

[0040] If the steady-state disturbance threshold is exceeded, the fast response unit is triggered, using its state information as a constraint, to output charging or discharging power in reverse according to the deviation value. Once the deviation value exceeds the steady-state disturbance threshold, the current state information of the fast response unit, including its state of charge, available capacity, and maximum allowable charging / discharging power, is immediately retrieved as the basis for judging its power compensation capability. Using this state information as a constraint, the type of compensation action of the fast response unit is determined based on the magnitude and direction of the deviation value. If the deviation is negative, indicating a decrease in renewable energy output leading to insufficient power, the fast response unit performs a discharging operation to provide supplementary power; if the deviation is positive, indicating an increase in renewable energy output leading to excess power, the fast response unit performs a charging operation to absorb excess energy and avoid impacting the power grid.

[0041] In actual control, the system matches a suitable target charging and discharging power value based on the magnitude of the deviation, while ensuring that this value does not exceed the current allowable power output range of the fast response unit, nor does it cause its state of charge to exceed the safe range. Once the adjustment command is generated, it is immediately sent to the fast response unit to complete the charging and discharging operation, thereby achieving second-level response and compensation for fluctuations in renewable energy output. The above adjustment mechanism operates continuously in each sampling cycle, possessing rapid detection, rapid decision-making, and rapid execution capabilities, effectively improving the adaptability and control capability of the energy storage system to short-term fluctuations in renewable energy output, and providing stable and continuous power output support for renewable energy power plants.

[0042] This invention divides the energy storage system into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which respectively undertake power regulation tasks at the millisecond, minute, and hourly levels, achieving coordinated control across multiple time scales and significantly improving system response speed and regulation accuracy. By incorporating intraday meteorological forecasts and load forecast data, new energy output forecast curves and power gap curves are established, enabling advance planning of the target power curve for the energy storage system and improving the foresight of energy storage dispatch and the capacity for new energy absorption. During operation, real-time monitoring of new energy output fluctuations automatically triggers the fast response unit for power compensation, and adaptively adjusts the response power according to the frequency and amplitude of disturbances, effectively suppressing the impact of short-term fluctuations on the grid frequency and ensuring stable system operation.

[0043] Example 2 A dynamic power control system for new energy power plants based on energy storage systems includes: Module division: The energy storage system is divided into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which are used for short-time frequency support, minute-level power smoothing, and hour-level energy migration, respectively. New energy output module: Based on the intraday meteorological forecast information, obtain the predicted new energy output curve, wherein the intraday meteorological forecast information includes light intensity forecast data and wind speed forecast data; The acquisition module compares the daily load forecast data with the new energy forecast output curve to obtain the daily power gap curve. Determination Module: Based on the intraday power gap curve and the state information of the energy storage system, determine the target output power curves of the slow migration unit and the medium speed regulation unit in the energy storage system for each intraday scheduling cycle; control the power output of the slow migration unit and the medium speed regulation unit according to the target output power curves; Triggering module: Monitors the fluctuation data of new energy output in real time, and triggers the fast response unit in the energy storage system to perform power compensation based on the fluctuation data.

[0044] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

[0045] The present invention has been described above with reference to embodiments thereof. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. The scope of the invention is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

[0046] Although embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions, and modifications can be made to the embodiments of the present invention without departing from the spirit and scope of the invention.

[0047] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A dynamic power control method for new energy power plants based on energy storage systems, characterized in that, include: The energy storage system is divided into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which are used for short-time frequency support, minute-level power smoothing, and hour-level energy migration, respectively. Based on the intraday meteorological forecast information, the predicted new energy output curve is obtained. The intraday meteorological forecast information includes light intensity forecast data and wind speed forecast data. By comparing the daily load forecast data with the new energy source forecast output curve, the daily power gap curve is obtained. Based on the intraday power deficit curve and the state information of the energy storage system, the target output power curves of the slow migration unit and the medium-speed regulation unit in the energy storage system are determined for each intraday scheduling cycle, including: The intraday power gap curve is decomposed to extract the slow-changing component containing hourly trend changes and the fast-changing component containing minute-level fluctuations. Acquire the status information of the slow migration unit and the medium speed regulation unit in the energy storage system. The status information includes their respective state of charge, available capacity and maximum charge and discharge power. Based on the slow-changing component, fast-changing component and state information, the power adjustment task in each scheduling cycle is allocated, and the target output power curves of the slow migration unit and the medium-speed adjustment unit in each scheduling cycle are generated respectively. The power output of the slow migration unit and the medium speed adjustment unit is controlled according to the target output power curve. Real-time monitoring of fluctuations in new energy output data triggers a rapid response unit in the energy storage system to perform power compensation based on the fluctuation data.

2. The dynamic power control method for new energy power plants based on energy storage systems according to claim 1, characterized in that, The division of the energy storage system into a fast response unit, a medium-speed regulation unit, and a slow migration unit includes: Energy storage systems are divided into fast response units, medium-speed regulation units, and slow migration units according to their response speed and capacity. The fast response unit uses an energy storage medium with a response time in the millisecond range, and the capacity ratio of the fast response unit is less than or equal to a first capacity ratio of the total installed capacity of the new energy power station. The medium-speed regulation unit uses an energy storage medium with a response time in the range of seconds to minutes, and the capacity ratio of the medium-speed regulation unit is between a first capacity ratio and a second capacity ratio. The slow migration unit uses an energy storage medium with a response time in the range of minutes to hours, and the capacity ratio of the slow migration unit is greater than or equal to the second capacity ratio.

3. The dynamic power control method for new energy power plants based on energy storage systems according to claim 1, characterized in that, The energy storage medium of the fast response unit is a supercapacitor, a lithium-titanium battery, or other energy storage medium with high power density; the energy storage medium of the medium speed regulation unit is a lithium iron phosphate battery, a ternary lithium battery, or a flow battery; and the energy storage medium of the slow migration unit is a flow battery, compressed air energy storage, or a large lithium battery pack.

4. The dynamic power control method for new energy power plants based on energy storage systems according to claim 1, characterized in that, Based on intraday weather forecasts, the predicted renewable energy output curves are obtained, including: The light intensity prediction data is input into a preset photovoltaic power prediction model, which is based on a neural network model, to obtain a photovoltaic output prediction curve. The wind speed prediction data is input into the wind power prediction model, which is based on a deep learning model, to obtain the wind power output prediction curve. The predicted power output curves of photovoltaic power and wind power are superimposed to obtain the predicted new energy power output curves.

5. The dynamic power control method for new energy power plants based on energy storage systems according to claim 1, characterized in that, The intraday power gap curve is decomposed to extract a slow-changing component containing hourly trend changes and a fast-changing component containing minute-level fluctuations, including: The intraday power gap curve is discretely sampled according to a fixed time step to form a discrete time series. The time series is subjected to moving average filtering, with the moving window width set to 1 hour, to obtain the slow-varying components representing hourly trend changes; By subtracting the slow-varying component from the discrete time series, the remaining part is obtained, which is the fast-varying component representing minute-level fluctuations.

6. The dynamic power control method for new energy power plants based on energy storage systems according to claim 1, characterized in that, The acquisition of status information for the slow migration unit and the medium-speed regulation unit in the energy storage system includes their respective states of charge, available capacity, and maximum charge / discharge power, including: Based on the state of charge, available capacity, and maximum charge / discharge power of the slow migration unit, determine the first output power range allowed for the current scheduling cycle of the slow migration unit; Based on the state of charge, available capacity, and maximum charge / discharge power of the medium-speed regulating unit, the second output power range allowed for the current scheduling cycle of the medium-speed regulating unit is determined.

7. The dynamic power control method for new energy power plants based on energy storage systems according to claim 6, characterized in that, Based on the slow-changing component, fast-changing component, and state information, power regulation tasks are allocated for each scheduling cycle, and target output power curves for the slow migration unit and the medium-speed regulation unit are generated for each scheduling cycle, including: Within the first output power range and the second output power range, the fast-change component is allocated to the medium-speed regulation unit; The allocation ratio of the slow-varying component to the slow-speed migration unit and the medium-speed adjustment unit is calculated. The calculation of the allocation ratio is based on the fact that while meeting the adjustment requirements of the slow-varying component and the fast-varying component, the target output power of the slow-varying unit changes the least with respect to the output power of the previous scheduling cycle. The target output power curve of the slow migration unit in each scheduling cycle is generated based on the slow variable component and the allocation ratio, and the target output power curve of the medium speed regulation unit in each scheduling cycle is generated based on the fast variable component, the slow variable component and the allocation ratio.

8. The dynamic power control method for new energy power plants based on energy storage systems according to claim 1, characterized in that, Real-time monitoring of fluctuations in renewable energy output, and triggering a rapid response unit in the energy storage system to perform power compensation based on the fluctuation data, including: Based on the fluctuation data and the target output power within the current scheduling period, the power deviation value for the current scheduling period is obtained; Determine whether the power deviation value exceeds the set steady-state disturbance threshold; If the steady-state disturbance threshold is exceeded, the fast response unit is triggered using the state information of the fast response unit as a constraint, and the charging or discharging power is output in reverse according to the deviation value.

9. A dynamic power control system for new energy power plants based on an energy storage system, characterized in that, include: Module division: The energy storage system is divided into a fast response unit, a medium-speed regulation unit, and a slow migration unit, which are used for short-time frequency support, minute-level power smoothing, and hour-level energy migration, respectively. New energy output module: Based on the intraday meteorological forecast information, obtain the predicted new energy output curve, wherein the intraday meteorological forecast information includes light intensity forecast data and wind speed forecast data; The acquisition module compares the daily load forecast data with the new energy forecast output curve to obtain the daily power gap curve. Determination Module: Based on the intraday power deficit curve and the state information of the energy storage system, determine the target output power curves of the slow migration unit and the medium-speed regulation unit in the energy storage system for each intraday scheduling cycle, including: The intraday power gap curve is decomposed to extract the slow-changing component containing hourly trend changes and the fast-changing component containing minute-level fluctuations. Acquire the status information of the slow migration unit and the medium speed regulation unit in the energy storage system. The status information includes their respective state of charge, available capacity and maximum charge and discharge power. Based on the slow-changing component, fast-changing component and state information, the power adjustment task in each scheduling cycle is allocated, and the target output power curves of the slow migration unit and the medium-speed adjustment unit in each scheduling cycle are generated respectively. The power output of the slow migration unit and the medium speed adjustment unit is controlled according to the target output power curve. Triggering module: Monitors the fluctuation data of new energy output in real time, and triggers the fast response unit in the energy storage system to perform power compensation based on the fluctuation data.

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