A method and system for coordinated frequency modulation control of thermal power-flywheel energy storage
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN THERMAL POWER RES INST CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-06-23
Smart Images
Figure CN122267808A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power system frequency regulation control technology, specifically relating to a thermal power-flywheel energy storage coordinated frequency regulation control method and system. Background Technology
[0002] As the proportion of new energy power generation such as wind and solar power in the power grid continues to increase, the randomness and volatility of their output pose a significant challenge to the stability of the power grid frequency. Traditional thermal power frequency regulation relies on adjusting the valve opening of the steam turbine unit to achieve output regulation, which is the core means of power grid frequency regulation, but it has significant limitations: on the one hand, thermal power units have mechanical inertia, and the action of steam valves and the transmission of steam pressure both require time, resulting in a lag in frequency regulation response; on the other hand, when the output of new energy sources increases sharply, the load reduction speed of thermal power units is slow, and they cannot free up grid connection space in time, thus leading to wind and solar curtailment.
[0003] Based on the above, the current frequency regulation of thermal power plants has the following specific problems: 1. Response lag problem: When the grid load suddenly increases by 500MW, such as when production lines in an industrial park start up simultaneously, the traditional thermal power frequency regulation system needs to receive the grid AGC and automatic power generation control command first, and then drive the high-pressure regulating valve of the steam turbine to increase the opening. During this process, it takes about 30-60 seconds for the steam pressure to be transferred from the boiler to the steam turbine. As a result, the thermal power output can only increase by 200MW within 1 minute after the command is issued. The grid frequency deviation once exceeds the allowable range of ±0.2Hz, triggering the low-frequency load shedding device to act, causing some users to experience short-term power outages.
[0004] 2. Wind and Solar Curtailment Issues: At 2 PM on a certain day, due to increased sunlight and wind speed, the total output of wind and solar power at a certain wind farm suddenly increased by 800MW. However, the minimum technical output of the thermal power cluster in the area is limited by the stability of boiler combustion and needs to be maintained at 60% of the rated output, approximately 1200MW. It was impossible to quickly reduce it to below 1000MW within 30 minutes, which forced the power grid dispatch center to instruct the wind farm to curtail 300MW of wind power and the solar power plant to curtail 150MW of solar power. The renewable energy consumption rate was only 43.75%.
[0005] In summary, how to avoid the lag in response of traditional thermal power frequency regulation systems and the resulting low renewable energy consumption rate leading to wind and electricity curtailment is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0006] The technical problem this invention aims to solve is to address the shortcomings of the prior art by providing a thermal power-flywheel energy storage coordinated frequency regulation control method and system. Through the coordinated operation of the millisecond-level response characteristics of flywheel energy storage and the stepped regulation logic of thermal power, it achieves rapid acceptance of grid frequency regulation demands, avoids excessive grid frequency deviations caused by response delays, and ensures grid operational stability. This also solves the technical problem of frequency regulation lag caused by mechanical inertia and the time-consuming transmission of steam pressure in traditional thermal power units.
[0007] This invention adopts the following technical solution: a method for coordinated frequency regulation control of thermal power and flywheel energy storage, comprising the following specific steps: S1 collects parameters from power grids, thermal power plants, flywheel energy storage, and new energy power stations; S2. Based on the previously collected synchronous data, the LSTM model is used to predict the grid frequency regulation demand, the thermal power regulation capacity is predicted based on thermal power parameters, and the available flywheel capacity is determined based on the flywheel SOC and the predicted value of new energy. S3 implements differentiated measures for emergency, routine, and renewable energy consumption scenarios to achieve efficient frequency regulation and renewable energy consumption under different operating conditions; S4 periodically evaluates the frequency regulation effect, updates the flywheel charging and discharging strategy in conjunction with new energy forecasts, and regularly conducts multi-objective optimization.
[0008] Preferably, S1 includes: Data preprocessing: The moving average filtering method is used to eliminate high-frequency noise during the parameter acquisition process, and the missing data is filled in by linear interpolation. Parameters from different sources are uniformly converted into a standard format. Real-time data synchronization: The preprocessed parameters are synchronized to the collaborative control platform in real time. The platform marks the received data with time sequence to align the parameters of the power grid, thermal power, flywheel, and new energy in the time dimension. Parameter anomaly tracing and self-healing: Based on the preprocessed parameter data, the system monitors in real time whether parameter fluctuations exceed the normal threshold and locates the source of the anomaly through data link diagnosis; at the same time, a deviation correction algorithm based on historical data is used to accurately correct the abnormal parameters.
[0009] Preferably, S1 further includes: Multi-dimensional parameters are collected in real time by sensors deployed at grid nodes, thermal power units, flywheel energy storage systems and new energy power plants; After diagnosing and locating the source of the anomaly through data link diagnostics, if the fault is identified as a sensor failure, the system will automatically switch to the backup sensor monitoring path; if the problem is a data transmission issue, the system will initiate local cached data retransmission.
[0010] Preferably, the parameters collected include: Frequency deviation, frequency change rate, AGC command amplitude and change rate on the power grid side; Actual output of the unit, valve opening, main steam pressure, and minimum or maximum technical output of the unit on the thermal power side; SOC, charge / discharge power limits, and response delay time on the flywheel energy storage side; Real-time output of wind power and real-time output of photovoltaic power on the new energy side.
[0011] Preferably, S2 includes: Based on the synchronous grid frequency deviation, frequency change rate and AGC command historical data, the LSTM model is used to predict the grid frequency regulation demand, including the predicted frequency deviation peak, frequency regulation duration and frequency regulation urgency. Based on the main steam pressure, current valve opening value, and minimum or maximum technical output of the thermal power unit, and combined with the load regulation rate model of the thermal power unit, the maximum increase in output, maximum decrease in output, and regulation delay time of the thermal power unit are predicted. Based on the current SOC value of flywheel energy storage, charging and discharging power limitations, and combined with the predicted output value of new energy sources, the available frequency regulation capacity of the flywheel is predicted.
[0012] Preferably, S3 includes: In response to emergency frequency regulation needs, when the grid is predicted or detected to be in an emergency frequency regulation state, the collaborative control platform prioritizes instructing the flywheel energy storage to respond with maximum charging and discharging power, while the platform issues step-by-step power output increase instructions to the thermal power units. To meet the needs of regular frequency regulation, a control logic of thermal power as the main force and flywheel as the auxiliary force is adopted. The collaborative control platform, based on the prediction results of the thermal power regulation capacity, instructs the thermal power unit to adjust its output according to the rated regulation rate; the flywheel energy storage tracks the actual deviation of the thermal power output and supplements the regulation gap with small power fluctuations; if the output of new energy sources fluctuates slightly, the flywheel will preferentially absorb or release electrical energy. New energy consumption scenario control: When a sudden increase in new energy output is detected and thermal power cannot quickly reduce load, the platform instructs the flywheel energy storage to switch to charging mode to absorb excess new energy power at maximum charging power; at the same time, it issues a slow load reduction instruction to thermal power units to gradually free up grid connection space; when the thermal power output drops to the target value and the new energy output tends to stabilize, the platform adjusts the charging power according to the flywheel SOC to avoid overcharging of the flywheel, thus realizing a closed loop of new energy flywheel temporary storage - thermal power capacity freeing up - grid consumption.
[0013] Preferably, S4 includes: The collaborative control platform evaluates the frequency modulation effect of the previous cycle and makes adaptive adjustments based on the evaluated indicators. Based on updated data of new energy output forecasts, the charging and discharging timing of flywheel energy storage is dynamically adjusted. The particle swarm optimization algorithm is used to periodically optimize the collaborative control strategy with the objective functions of minimizing frequency regulation cost, minimizing renewable energy curtailment rate, and maximizing frequency stability.
[0014] Preferably, S4 further includes: Before implementing the optimized control strategy, a digital twin simulation model is built to simulate the implementation effect of the optimized strategy after inputting the current grid status, thermal power and flywheel energy storage parameters and future prediction data, and to pre-evaluate the indicators of frequency stability, economy and renewable energy absorption rate.
[0015] Preferably, if the pre-evaluation result does not meet the preset target, the particle swarm optimization algorithm is returned to readjust the parameters; if the pre-evaluation result meets the target, the optimization strategy is sent to the control terminal to avoid the decline in frequency modulation effect caused by blindly executing the optimization scheme, and to improve the accuracy and reliability of strategy optimization.
[0016] Another technical solution of the present invention is a thermal power-flywheel energy storage coordinated frequency regulation control system, comprising: The real-time monitoring and preprocessing module is used to collect key parameters of power grid, thermal power, flywheel energy storage, and new energy power plants, providing reliable data support for subsequent prediction and control. The frequency regulation demand and resource capacity prediction module is used to predict the grid frequency regulation demand using an LSTM model based on the previous synchronous data, and to predict its regulation capacity in combination with thermal power parameters. It also judges the available flywheel capacity based on the flywheel SOC and the predicted value of new energy sources, so as to understand the supply and demand matching situation in advance and provide a basis for decision-making for the formulation of coordinated control strategies. The scenario-specific thermal power-flywheel energy storage collaborative control module is used to implement differentiated policies for three scenarios: emergency, conventional, and new energy consumption. The collaborative control strategy dynamic optimization module is used to periodically evaluate the frequency modulation effect, adaptively adjust the control parameters, and update the flywheel charging and discharging strategy in conjunction with new energy prediction; and regularly carry out multi-objective optimization.
[0017] Compared with the prior art, the present invention has at least the following beneficial effects: By combining the millisecond-level response characteristics of flywheel energy storage with the stepped regulation logic of thermal power, the frequency regulation lag caused by mechanical inertia and steam pressure transmission time of traditional thermal power units can be compensated, so as to realize the rapid acceptance of the grid frequency regulation demand, avoid the grid frequency deviation caused by response delay, and ensure the stability of grid operation. By using flywheel energy storage to temporarily store excess electricity during a surge in new energy sources, combined with the regulation method of thermal power gradually reducing load to free up grid connection space, the problem of insufficient grid connection space caused by a surge in new energy output and the inability of thermal power to quickly reduce load is solved, thus avoiding the abandonment of new energy due to lack of consumption space and effectively improving the level of new energy consumption. By using a differentiated control logic that is dominated by thermal power and assisted by flywheel, it is possible to reduce the mechanical losses caused by frequent start-ups or large load adjustments in thermal power plants, reduce the risk of failure caused by long-term high-intensity regulation of thermal power equipment, extend the service life of thermal power equipment, and reduce the maintenance burden caused by equipment repair and replacement. By using LSTM models to predict grid frequency regulation demand and the resource capacity of thermal power and flywheel energy storage, and combining this with a dynamically optimized collaborative control strategy, the traditional simple logic of using flywheels to fill the gap caused by thermal power lag can be replaced. This enables a shift from passive response to proactive planning, improves the adaptability of control strategies, and solves the problem of insufficient control accuracy caused by the lack of prediction in existing solutions.
[0018] In summary, the thermal power-flywheel energy storage coordinated frequency regulation control method and system of the present invention achieves rapid acceptance of grid frequency regulation needs through the coordinated cooperation of the millisecond-level response characteristics of flywheel energy storage and the stepped regulation logic of thermal power, making up for the frequency regulation lag defects caused by mechanical inertia and steam pressure transmission time of traditional thermal power units, avoiding grid frequency deviation exceeding the standard due to response delay, and ensuring grid operation stability.
[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the following description of the relative embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a schematic diagram of the overall framework of the present invention; Figure 2 This is a schematic diagram of the real-time monitoring and preprocessing module framework of the present invention; Figure 3 This is a schematic diagram of the frequency modulation demand and resource capability prediction module framework of the present invention; Figure 4 This is a schematic diagram of the framework of the thermal power flywheel energy storage collaborative control module for different scenarios according to the present invention; Figure 5 This is a schematic diagram of the dynamic optimization module framework for the collaborative control strategy of the present invention. Detailed Implementation
[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "one side," "one end," and "one side," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.
[0024] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] This invention provides a thermal power-flywheel energy storage coordinated frequency regulation control method and system. By coordinating the millisecond-level response characteristics of flywheel energy storage with the stepped regulation logic of thermal power, it can quickly meet the frequency regulation needs of the power grid, make up for the frequency regulation lag defects caused by mechanical inertia and steam pressure transmission time of traditional thermal power units, avoid excessive grid frequency deviation caused by response delay, and ensure the stability of power grid operation.
[0030] Please see Figures 1 to 5 This invention provides a method for coordinated frequency regulation control of thermal power and flywheel energy storage, comprising the following specific steps: S1, Real-time monitoring and preprocessing: Collects key parameters from power grids, thermal power plants, flywheel energy storage, and new energy power stations. After filtering, completion, and format unification preprocessing, the data is synchronized to the control platform via a 5G private network with low latency to ensure accurate data alignment and provide reliable data support for subsequent prediction and control. The specific steps of S1 are as follows: S11, Parameter Acquisition: including: frequency deviation, frequency change rate, AGC command amplitude and change rate on the grid side; actual unit output, valve opening, main steam pressure, and minimum / maximum technical output of the unit on the thermal power side; SOC (State of Charge), charging and discharging power limit, and response delay time on the flywheel energy storage side; real-time output of wind power, real-time output of photovoltaic power, and predicted output value for the next 15 minutes on the new energy side.
[0031] Specifically, the aforementioned multi-dimensional parameters can be collected in real time using sensors and corresponding monitoring equipment deployed at grid nodes, thermal power units, flywheel energy storage systems, and new energy power plants. The specific data collection methods and devices used are common knowledge well-known to those skilled in the art and will not be elaborated upon here.
[0032] S12, Data Preprocessing: The moving average filtering method is used to eliminate high-frequency noise during parameter acquisition, such as frequency data deviation of ±0.01Hz caused by sensor fluctuations; and for missing data, such as 1-2 seconds of missing output data caused by brief sensor offline, linear interpolation is used to fill in the missing data; and parameters from different sources are uniformly converted to a standard format, such as power unit uniformly set to MW and time unit uniformly set to seconds, to avoid control delay caused by data format incompatibility; S13, Real-time Data Synchronization: Establish a low-latency data transmission channel based on a 5G private network to synchronize preprocessed parameters to the collaborative control platform in real time, with synchronization latency controlled within 50ms; and enable the platform to perform time-series marking on the received data to ensure that the parameters of the power grid, thermal power, flywheel, and new energy are aligned in the time dimension, providing an accurate data foundation for subsequent prediction and control; S14, Parameter Anomaly Tracing and Self-Healing: Based on the preprocessed parameter data, monitor in real time whether parameter fluctuations exceed normal thresholds, such as sudden changes in frequency deviation or gaps in thermal power output data. Locate the source of the anomaly through data link diagnosis, such as sensor failure or transmission link interruption. If it is a sensor failure, automatically switch to the backup sensor monitoring path. If it is a data transmission problem, start local cached data retransmission. At the same time, use a deviation correction algorithm based on historical data to accurately correct abnormal parameters, avoid abnormal data from causing subsequent prediction deviations or control command errors, and ensure the continuous reliability of the data link.
[0033] S2, Frequency Regulation Demand and Resource Capacity Prediction: Based on the previous synchronous data, the LSTM model is used to predict the grid frequency regulation demand, and the thermal power parameters are combined to predict its regulation capacity. The flywheel available capacity is judged based on the flywheel SOC and the predicted value of new energy sources, so as to understand the supply and demand matching situation in advance and provide a decision-making basis for the formulation of coordinated control strategies. The specific steps of S2 are as follows: S21, Grid Frequency Regulation Demand Forecast: Based on the synchronous grid frequency deviation, frequency change rate, and historical AGC command data, the LSTM model (Long Short-Term Memory network model) is used to predict the grid frequency regulation demand within the next 10-15 minutes, including: predicting the peak frequency deviation (e.g., predicting that the frequency deviation will reach -0.18Hz in the next 8 minutes, requiring an additional 400MW of power output); the duration of frequency regulation (e.g., predicting that the additional power output demand will last for 12 minutes); and the urgency of frequency regulation, which is divided into three levels: emergency frequency regulation, regular frequency regulation, and standby frequency regulation, depending on whether the frequency deviation exceeds the ±0.15Hz threshold. S22, Prediction of Thermal Power Regulation Capacity: Based on the main steam pressure, current value of steam valve opening, and minimum / maximum technical output of the thermal power unit, and combined with the load regulation rate model of the thermal power unit, such as a 300MW unit in the range of 70%-90% of rated output with a load regulation rate of 2% / min, the maximum increase in output, maximum decrease in output, and regulation delay time of the thermal power unit in the next 10 minutes are predicted. For example, if it is predicted that the thermal power unit can increase its output by a maximum of 300MW in the next 5 minutes, the regulation delay is 15 seconds. S23, Flywheel Energy Storage Availability Prediction: Based on the current SOC value of the flywheel energy storage (e.g., 80% SOC) and charging / discharging power limitations (e.g., maximum charging / discharging power of 200MW), and combined with the predicted output of new energy sources in the next 15 minutes, predict the available frequency regulation capacity of the flywheel. If it is predicted that the output of new energy sources will increase sharply, reserve 50% of the flywheel energy storage capacity to absorb excess electricity and avoid wind and solar curtailment. If it is predicted that the grid will need to increase its output, calculate the maximum energy that the flywheel can release during the frequency regulation period. For example, when the SOC drops from 80% to 20%, it can release 12MWh of energy, which can sustain 200MW of power for 360 seconds.
[0034] S3, scenario-specific thermal power-flywheel energy storage coordinated control: differentiated policies for three scenarios: emergency, conventional, and new energy consumption. In emergency scenarios, the flywheel is prioritized to fill the gap and thermal power is adjusted in stages. In conventional scenarios, thermal power takes the lead and the flywheel is assisted. In new energy consumption scenarios, the flywheel temporarily stores energy and thermal power makes up capacity, so as to achieve efficient frequency regulation and new energy consumption under different operating conditions. The specific steps of S3 are as follows: S31, Emergency Frequency Regulation Scenario Control, Frequency Deviation > ±0.15Hz: When the grid is predicted or monitored to be in an emergency frequency regulation state, such as a sudden increase in load causing a frequency deviation of -0.2Hz, the collaborative control platform prioritizes instructing the flywheel energy storage to respond with maximum charging and discharging power. If additional output is required, the flywheel reaches its maximum discharge power, such as 200MW, within 100ms, quickly filling the gap caused by the lag in thermal power response. At the same time, the platform issues step-by-step output increase instructions to the thermal power units, such as increasing output by 50MW in the first 15 seconds, 100MW in 15-30 seconds, and 150MW in 30-60 seconds, to avoid a sudden drop in steam pressure caused by the sudden opening of the thermal power steam valve. When the thermal power output reaches the predicted maximum increase, such as 300MW, and the grid frequency recovers to within ±0.05Hz, the platform instructs the flywheel to gradually reduce the discharge power, transferring the frequency regulation control to the thermal power. S32, for conventional frequency regulation scenarios, 0.05Hz < frequency deviation ≤ ±0.15Hz: For conventional frequency regulation needs, such as a frequency deviation of -0.1Hz due to slow load changes, a control logic dominated by thermal power and assisted by flywheel is adopted. The platform predicts the thermal power unit's regulation capacity and instructs it to adjust its output at the rated regulation rate, such as 2% / min. Simultaneously, the flywheel energy storage tracks the actual deviation of the thermal power output. For example, if the thermal power unit instructs to increase its output by 100MW, but only increases by 80MW within 10 seconds, a small power fluctuation of 10-50MW is used to supplement the regulation gap, avoiding equipment damage caused by frequent fine-tuning of the thermal power unit. If the output of new energy sources fluctuates slightly, such as a sudden drop of 50MW in wind power output, the flywheel prioritizes absorbing or releasing energy, eliminating the need for thermal power unit adjustments. S33, New Energy Consumption Scenario Control, New Energy Output Surge > 10%: When a surge in new energy output is detected, such as wind power output increasing from 200MW to 400MW, and thermal power cannot quickly reduce load, the platform instructs the flywheel energy storage to switch to charging mode, absorbing excess new energy power at maximum charging power, such as 200MW; simultaneously, it issues a slow load reduction instruction to the thermal power unit, such as reducing from 1200MW to 1100MW at a rate of 1% / min, gradually freeing up grid connection space; when the thermal power output drops to the target value and the new energy output tends to stabilize, the platform adjusts the charging power according to the flywheel SOC, such as when the SOC rises to 90%, to avoid overcharging the flywheel, thus realizing a closed loop of new energy flywheel temporary storage - thermal power capacity creation - grid consumption.
[0035] S4, Dynamic optimization of collaborative control strategy: Periodically evaluate the frequency regulation effect to adaptively adjust control parameters; update the flywheel charging and discharging strategy in conjunction with new energy prediction; and periodically use particle swarm optimization algorithm to carry out multi-objective optimization to balance cost, curtailment rate and stability, continuously optimize the strategy, and ensure the long-term economic efficiency and reliability of operation. The specific steps of S4 are as follows: S41, Adaptive Adjustment of Control Parameters: The collaborative control platform evaluates the frequency regulation effect of the previous cycle every 5 minutes. Indicators include: duration of frequency deviation exceeding the standard (target ≤10 seconds), flywheel energy storage charging and discharging efficiency (target ≥90%), and thermal power load adjustment range (target ≤5% of rated output). Based on the evaluated indicators, adaptive adjustments are made. If the duration of frequency deviation exceeding the standard is >10 seconds, the initial response power of the flywheel is increased, for example, from 200MW to 250MW; if the thermal power load adjustment range is >5%, the thermal power stepped adjustment time interval is extended, for example, from 15 seconds to 20 seconds, thus achieving adaptive optimization of control parameters. S42, Flywheel charging and discharging strategy optimization: Based on the updated data of new energy output forecast, such as updating the forecast value for the next 15 minutes every 10 minutes, the charging and discharging timing of flywheel energy storage is dynamically adjusted; if it is predicted that the new energy output will drop sharply in the next 5 minutes, the flywheel SOC will be charged from 60% to 80% in advance to reserve discharge capacity; if it is predicted that there will be no major frequency regulation demand of the power grid in the next 10 minutes, the flywheel SOC will be maintained in the optimal range of 50%-70% to avoid life loss caused by overcharging and over-discharging; S43, Multi-objective optimization verification: The particle swarm optimization algorithm is adopted, with the objective functions of minimizing frequency regulation cost, minimizing renewable energy curtailment rate, and maximizing frequency stability. The coordinated control strategy is periodically optimized once a day. In addition, the optimization process considers the coal consumption cost of thermal power, such as a 1% load reduction that reduces coal consumption by 2g / kWh, the operation and maintenance cost of flywheel energy storage, such as a charge / discharge cost of 0.5 yuan / kWh, and the renewable energy curtailment loss, such as a 1MWh wind power curtailment loss of 0.3 yuan, in order to output the optimal control strategy parameters and ensure the long-term economic efficiency and reliability of operation. S44, Pre-evaluation of optimization effect: Before implementing the optimized control strategy, a digital twin simulation model is built to simulate the implementation effect of the optimization strategy after inputting the current grid status, thermal power and flywheel energy storage parameters, and predicted data for the next 15 minutes. It also evaluates frequency stability, simulates the duration of frequency deviation exceeding the standard, economic efficiency, simulates the total cost of thermal power coal consumption and flywheel operation and maintenance, and the renewable energy absorption rate, i.e., the indicators of wind and solar curtailment. If the pre-evaluation results do not meet the preset targets, such as the duration of frequency deviation exceeding the standard > 5 seconds or the absorption rate < 95%, the particle swarm optimization algorithm is returned to readjust the parameters. If the targets are met, the optimization strategy is sent to the control terminal to avoid the decline in frequency regulation effect caused by blindly implementing the optimization scheme and to improve the accuracy and reliability of strategy optimization.
[0036] Digital twin simulation models are a mature digital technology model. By constructing a digital virtual image in a computer that is consistent with the real power grid, thermal power unit, flywheel energy storage, and new energy power station, the operating characteristics, parameter changes and interaction relationships of real equipment can be replicated. The execution effect of various strategies can be simulated in a virtual environment, and the advantages and disadvantages can be verified in advance.
[0037] Please refer to a thermal power-flywheel energy storage coordinated frequency regulation control system. Figure 1 ,include: The real-time monitoring and preprocessing module is used to collect key parameters of power grid, thermal power, flywheel energy storage, and new energy power stations. After filtering, completion, and format unification preprocessing, the data is synchronized to the control platform with low latency through the 5G private network to ensure accurate data alignment and provide reliable data support for subsequent prediction and control. The frequency regulation demand and resource capacity prediction module is used to predict the grid frequency regulation demand using an LSTM model based on the previous synchronous data, and to predict its regulation capacity in combination with thermal power parameters. It also judges the available flywheel capacity based on the flywheel SOC and the predicted value of new energy sources, so as to understand the supply and demand matching situation in advance and provide a basis for decision-making for the formulation of coordinated control strategies. The scenario-specific thermal power-flywheel energy storage collaborative control module is used to implement differentiated policies for three scenarios: emergency, conventional, and new energy consumption. In emergency scenarios, the flywheel is prioritized to fill the gap and thermal power is adjusted in stages. In conventional scenarios, thermal power takes the lead and the flywheel is assisted. In new energy consumption scenarios, the flywheel temporarily stores energy and thermal power makes up capacity, so as to achieve efficient frequency regulation and new energy consumption under different operating conditions. The collaborative control strategy dynamic optimization module is used to periodically evaluate the frequency regulation effect and adaptively adjust the control parameters; it also combines new energy prediction to update the flywheel charging and discharging strategy; and it periodically uses particle swarm optimization algorithm to carry out multi-objective optimization to balance cost, curtailment rate and stability, continuously optimize the strategy, and ensure the long-term economic efficiency and reliability of operation.
[0038] Please see Figure 2 The real-time monitoring and preprocessing module includes: The parameter acquisition unit is used to collect the following parameters: frequency deviation, frequency change rate, AGC command amplitude and change rate on the grid side; actual unit output, valve opening, main steam pressure, and minimum / maximum technical output of the unit on the thermal power side; SOC, charging and discharging power limit, and response delay time on the flywheel energy storage side; and real-time wind power output, real-time photovoltaic power output, and power output prediction value for the next 15 minutes on the new energy side.
[0039] Specifically, the aforementioned multi-dimensional parameters can be collected in real time using sensors and corresponding monitoring equipment deployed at grid nodes, thermal power units, flywheel energy storage systems, and new energy power plants. The specific data collection methods and devices used are common knowledge well-known to those skilled in the art and will not be elaborated upon here.
[0040] The data preprocessing unit is used to eliminate high-frequency noise during parameter acquisition, such as frequency data deviation of ±0.01Hz caused by sensor fluctuations, by using the moving average filtering method; and to fill in missing data, such as 1-2 seconds of missing output data caused by brief sensor offline, by using linear interpolation; and to convert parameters from different sources into a standard format, such as power unit to MW and time unit to second, to avoid control delay caused by data format incompatibility. The real-time data synchronization unit is used to establish a low-latency data transmission channel based on the 5G private network to synchronize the preprocessed parameters to the collaborative control platform in real time, with the synchronization delay controlled within 50ms; and to enable the platform to perform time-series marking on the received data to ensure that the parameters of the power grid, thermal power, flywheel, and new energy are aligned in the time dimension, providing an accurate data foundation for subsequent prediction and control; The parameter anomaly tracing and self-healing unit is used to monitor whether parameter fluctuations exceed normal thresholds in real time based on preprocessed parameter data, such as sudden changes in frequency deviation or gaps in thermal power output data. It also uses data link diagnosis to locate the source of the anomaly, such as sensor failure or transmission link interruption. If it is a sensor failure, it automatically switches to the backup sensor monitoring path; if it is a data transmission problem, it initiates local cached data retransmission. At the same time, it uses a deviation correction algorithm based on historical data to accurately correct abnormal parameters, avoiding subsequent prediction deviations or control command errors caused by abnormal data, and ensuring the continuous reliability of the data link.
[0041] Please see Figure 3 The frequency modulation demand and resource capacity prediction module includes: The power grid frequency regulation demand prediction unit is used to predict the power grid frequency regulation demand within the next 10-15 minutes based on the synchronous power grid frequency deviation, frequency change rate, and historical data of AGC commands, using an LSTM (Long Short-Term Memory) network model. This includes: predicting the peak frequency deviation (e.g., predicting that the frequency deviation will reach -0.18Hz in the next 8 minutes, requiring an additional 400MW of power output); predicting the duration of frequency regulation (e.g., predicting that the additional power output demand will last for 12 minutes); and predicting the urgency of frequency regulation, which is divided into three levels: emergency frequency regulation, regular frequency regulation, and standby frequency regulation, depending on whether the frequency deviation exceeds the ±0.15Hz threshold. The thermal power regulation capacity prediction unit is used to predict the maximum increase in output, the maximum decrease in output, and the regulation delay time of thermal power in the next 10 minutes based on the main steam pressure, current value of steam valve opening, and minimum / maximum technical output of the unit, combined with the load regulation rate model of the thermal power unit. For example, if a 300MW unit is in the range of 70%-90% of rated output, the load regulation rate is 2% / min. The flywheel energy storage availability prediction unit is used to predict the available frequency regulation capacity of the flywheel based on the current SOC value of the flywheel energy storage, such as the current SOC being 80%, the charging and discharging power limit, such as the maximum charging and discharging power being 200MW, and the predicted value of renewable energy output for the next 15 minutes. If it is predicted that renewable energy output will increase sharply, 50% of the flywheel energy storage capacity is reserved to absorb excess electricity and avoid wind and solar curtailment. If it is predicted that the grid will need to increase its output, the maximum energy that the flywheel can release during the frequency regulation period is calculated. For example, when the SOC drops from 80% to 20%, 12MWh of energy can be released, which can sustain 200MW of power for 360 seconds.
[0042] Please see Figure 4 The scenario-specific thermal power-flywheel energy storage coordinated control module includes: The emergency frequency regulation scenario control unit, with a frequency deviation > ±0.15Hz, is used to predict or monitor when the power grid is in an emergency frequency regulation state, such as when a sudden load increase causes a frequency deviation of -0.2Hz. The collaborative control platform prioritizes instructing the flywheel energy storage to respond with maximum charging and discharging power. If increased output is required, the flywheel reaches its maximum discharge power, such as 200MW, within 100ms, quickly filling the gap caused by the lag in thermal power response. Simultaneously, the platform issues step-by-step output increase instructions to the thermal power units, such as increasing output by 50MW in the first 15 seconds, 100MW in 15-30 seconds, and 150MW in 30-60 seconds, to avoid a sudden drop in steam pressure caused by the sudden opening of the thermal power unit's steam valves. When the thermal power output reaches the predicted maximum increase, such as 300MW, and the grid frequency recovers to within ±0.05Hz, the platform instructs the flywheel to gradually reduce its discharge power, transferring frequency regulation control to the thermal power unit. The control unit for conventional frequency regulation scenarios has a frequency deviation of 0.05Hz < ±0.15Hz. It is used to address conventional frequency regulation needs, such as a frequency deviation of -0.1Hz caused by slow load changes. It adopts a control logic dominated by thermal power and assisted by flywheel. The platform predicts the thermal power unit's regulation capacity and instructs the thermal power unit to adjust its output at the rated regulation rate, such as 2% / min. At the same time, the flywheel energy storage tracks the actual deviation of the thermal power output. For example, if the thermal power unit is instructed to increase its output by 100MW, but only increases its output by 80MW within 10 seconds, the small power fluctuation of 10-50MW is used to supplement the regulation gap, avoiding equipment damage caused by frequent fine-tuning of thermal power. If the output of new energy sources fluctuates slightly, such as a sudden drop of 50MW in wind power output, the flywheel will prioritize absorbing or releasing electrical energy, without requiring thermal power unit adjustments. The renewable energy consumption scenario control unit detects a sudden increase in renewable energy output exceeding 10%. When such an increase is detected, such as wind power output increasing from 200MW to 400MW, and thermal power cannot quickly reduce its load, the platform instructs the flywheel energy storage to switch to charging mode, absorbing excess renewable energy at maximum charging power (e.g., 200MW). Simultaneously, it issues a slow load reduction instruction to the thermal power units, such as reducing from 1200MW to 1100MW at a rate of 1% / min, gradually freeing up grid connection space. When thermal power output drops to the target value and renewable energy output stabilizes, the platform adjusts the charging power based on the flywheel's State of Charge (SOC), such as when the SOC rises to 90%, to prevent overcharging of the flywheel, thus achieving a closed loop of renewable energy flywheel temporary storage, thermal power capacity expansion, and grid absorption.
[0043] Please see Figure 5 The collaborative control strategy dynamic optimization module includes: The adaptive adjustment unit for control parameters is used to enable the collaborative control platform to evaluate the frequency regulation effect of the previous 5-minute cycle every 5 minutes. The indicators include: the duration of frequency deviation exceeding the standard (target ≤10 seconds), the flywheel energy storage charging and discharging efficiency (target ≥90%), and the thermal power load adjustment range (target ≤5% of rated output). Based on the evaluated indicators, the unit makes adaptive adjustments. If the duration of frequency deviation exceeding the standard is >10 seconds, the initial response power of the flywheel is increased, such as from 200MW to 250MW. If the thermal power load adjustment range is >5%, the thermal power stepped adjustment time interval is extended, such as from 15 seconds to 20 seconds, to achieve adaptive optimization of control parameters. The flywheel charging and discharging strategy optimization unit is used to dynamically adjust the charging and discharging timing of flywheel energy storage based on updated data of new energy output forecasts, such as updating the forecast value for the next 15 minutes every 10 minutes. If it is predicted that the new energy output will drop sharply in the next 5 minutes, the flywheel SOC will be charged from 60% to 80% in advance to reserve discharge capacity. If it is predicted that there will be no major frequency regulation demand of the grid in the next 10 minutes, the flywheel SOC will be maintained in the optimal range of 50%-70% to avoid life loss caused by overcharging and over-discharging. The multi-objective optimization verification unit is used to periodically optimize the collaborative control strategy using the particle swarm optimization algorithm, with the objectives of minimizing frequency regulation cost, minimizing renewable energy curtailment rate, and maximizing frequency stability, once a day. During the optimization process, the unit considers the coal consumption cost of thermal power plants (e.g., a 1% load reduction reduces coal consumption by 2g / kWh), the operation and maintenance cost of flywheel energy storage (e.g., a charge / discharge cost of 0.5 yuan / kWh), and the renewable energy curtailment loss (e.g., a loss of 0.3 yuan per MWh of wind power curtailment). The goal is to output the optimal control strategy parameters to ensure the long-term economic efficiency and reliability of the operation. The optimization effect pre-evaluation unit is used to build a digital twin simulation model before executing the optimized control strategy. This model simulates the implementation effect of the optimization strategy after inputting the current grid status, thermal power and flywheel energy storage parameters, and predicted data for the next 15 minutes. It evaluates frequency stability, simulates the duration of frequency deviation exceeding the standard, economic efficiency, the total cost of thermal power coal consumption and flywheel operation and maintenance, the renewable energy absorption rate, and the amount of wind and solar curtailment. If the pre-evaluation results do not meet the preset targets, such as a frequency deviation exceeding the standard for more than 5 seconds or an absorption rate of less than 95%, the system returns to the particle swarm optimization algorithm to readjust the parameters. If the targets are met, the optimization strategy is sent to the control terminal to avoid a decline in frequency regulation performance due to blindly implementing the optimization scheme, thus improving the accuracy and reliability of the strategy optimization.
[0044] In summary, the present invention provides a thermal power-flywheel energy storage coordinated frequency regulation control method and system. By coordinating the millisecond-level response characteristics of flywheel energy storage with the stepped regulation logic of thermal power, it enables rapid acceptance of grid frequency regulation demands, compensates for the frequency regulation lag defects caused by mechanical inertia and steam pressure transmission time in traditional thermal power units, avoids excessive grid frequency deviation caused by response delay, and ensures grid operation stability.
[0045] 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 to the technical solution 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 coordinated frequency regulation control of thermal power plant and flywheel energy storage, characterized in that, The specific steps are as follows: S1 collects parameters from power grids, thermal power plants, flywheel energy storage, and new energy power stations; S2. Based on the previously collected synchronous data, the LSTM model is used to predict the grid frequency regulation demand, the thermal power regulation capacity is predicted based on thermal power parameters, and the available flywheel capacity is determined based on the flywheel SOC and the predicted value of new energy. S3 implements differentiated measures for emergency, routine, and renewable energy consumption scenarios to achieve efficient frequency regulation and renewable energy consumption under different operating conditions; S4 periodically evaluates the frequency regulation effect, updates the flywheel charging and discharging strategy in conjunction with new energy forecasts, and regularly conducts multi-objective optimization.
2. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 1, characterized in that, S1 includes: Data preprocessing: The moving average filtering method is used to eliminate high-frequency noise during the parameter acquisition process, and the missing data is filled in by linear interpolation. Parameters from different sources are uniformly converted into a standard format. Real-time data synchronization: The preprocessed parameters are synchronized to the collaborative control platform in real time. The platform marks the received data with time sequence to align the parameters of the power grid, thermal power, flywheel, and new energy in the time dimension. Parameter anomaly tracing and self-healing: Based on the preprocessed parameter data, the system monitors in real time whether parameter fluctuations exceed the normal threshold and locates the source of the anomaly through data link diagnosis; at the same time, a deviation correction algorithm based on historical data is used to accurately correct the abnormal parameters.
3. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 2, characterized in that, S1 further includes: Multi-dimensional parameters are collected in real time by sensors deployed at grid nodes, thermal power units, flywheel energy storage systems and new energy power plants; After diagnosing and locating the source of the anomaly through data link diagnostics, if the fault is identified as a sensor failure, the system will automatically switch to the backup sensor monitoring path; if the problem is a data transmission issue, the system will initiate local cached data retransmission.
4. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 1, characterized in that, The parameters collected include: Frequency deviation, frequency change rate, AGC command amplitude and change rate on the power grid side; Actual output of the unit, valve opening, main steam pressure, and minimum or maximum technical output of the unit on the thermal power side; SOC, charge / discharge power limits, and response delay time on the flywheel energy storage side; Real-time output of wind power and real-time output of photovoltaic power on the new energy side.
5. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 1, characterized in that, S2 includes: Based on the synchronous grid frequency deviation, frequency change rate and AGC command historical data, the LSTM model is used to predict the grid frequency regulation demand, including the predicted frequency deviation peak, frequency regulation duration and frequency regulation urgency. Based on the main steam pressure, current valve opening value, and minimum or maximum technical output of the thermal power unit, and combined with the load regulation rate model of the thermal power unit, the maximum increase in output, maximum decrease in output, and regulation delay time of the thermal power unit are predicted. Based on the current SOC value of flywheel energy storage, charging and discharging power limitations, and combined with the predicted output value of new energy sources, the available frequency regulation capacity of the flywheel is predicted.
6. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 1, characterized in that, S3 includes: In response to emergency frequency regulation needs, when the grid is predicted or detected to be in an emergency frequency regulation state, the collaborative control platform prioritizes instructing the flywheel energy storage to respond with maximum charging and discharging power, while the platform issues step-by-step power output increase instructions to the thermal power units. To meet the needs of regular frequency regulation, a control logic of thermal power as the main force and flywheel as the auxiliary force is adopted. The collaborative control platform, based on the prediction results of the thermal power regulation capacity, instructs the thermal power unit to adjust its output according to the rated regulation rate; the flywheel energy storage tracks the actual deviation of the thermal power output and supplements the regulation gap with small power fluctuations; if the output of new energy sources fluctuates slightly, the flywheel will preferentially absorb or release electrical energy. New energy consumption scenario control: When a sudden increase in new energy output is detected and thermal power cannot quickly reduce load, the platform instructs the flywheel energy storage to switch to charging mode to absorb excess new energy power at maximum charging power; at the same time, it issues a slow load reduction instruction to thermal power units to gradually free up grid connection space; when the thermal power output drops to the target value and the new energy output tends to stabilize, the platform adjusts the charging power according to the flywheel SOC to avoid overcharging of the flywheel, thus realizing a closed loop of new energy flywheel temporary storage - thermal power capacity freeing up - grid consumption.
7. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 1, characterized in that, S4 includes: The collaborative control platform evaluates the frequency modulation effect of the previous cycle and makes adaptive adjustments based on the evaluated indicators. Based on updated data of new energy output forecasts, the charging and discharging timing of flywheel energy storage is dynamically adjusted. The particle swarm optimization algorithm is used to periodically optimize the collaborative control strategy with the objective functions of minimizing frequency regulation cost, minimizing renewable energy curtailment rate, and maximizing frequency stability.
8. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 7, characterized in that, S4 further includes: Before implementing the optimized control strategy, a digital twin simulation model is built to simulate the implementation effect of the optimized strategy after inputting the current grid status, thermal power and flywheel energy storage parameters and future prediction data, and to pre-evaluate the indicators of frequency stability, economy and renewable energy absorption rate.
9. The thermal power-flywheel energy storage coordinated frequency regulation control method according to claim 8, characterized in that, If the pre-evaluation results do not meet the preset target, the particle swarm optimization algorithm is returned to readjust the parameters; if the pre-evaluation results meet the target, the optimization strategy is sent to the control terminal to avoid the decline in frequency modulation effect caused by blindly executing the optimization scheme, and to improve the accuracy and reliability of strategy optimization.
10. A thermal power-flywheel energy storage coordinated frequency regulation control system, characterized in that, include: The real-time monitoring and preprocessing module is used to collect key parameters of power grid, thermal power, flywheel energy storage, and new energy power plants, providing reliable data support for subsequent prediction and control. The frequency regulation demand and resource capacity prediction module is used to predict the grid frequency regulation demand using an LSTM model based on the previous synchronous data, and to predict its regulation capacity in combination with thermal power parameters. It also judges the available flywheel capacity based on the flywheel SOC and the predicted value of new energy sources, so as to understand the supply and demand matching situation in advance and provide a basis for decision-making for the formulation of coordinated control strategies. The scenario-specific thermal power-flywheel energy storage collaborative control module is used to implement differentiated policies for three scenarios: emergency, conventional, and new energy consumption. The collaborative control strategy dynamic optimization module is used to periodically evaluate the frequency modulation effect, adaptively adjust the control parameters, and update the flywheel charging and discharging strategy in conjunction with new energy prediction; and regularly carry out multi-objective optimization.