Flywheel energy storage auxiliary thermal power unit agc frequency modulation method, device and medium

Abstract

The application discloses a flywheel energy storage auxiliary thermal power unit AGC frequency modulation method and device and medium, relates to the AGC frequency modulation technical field, and the method comprises the following steps: preprocessing original AGC instruction data to obtain target AGC instruction data; the target AGC instruction data is segmented to obtain several AGC instruction stages;Flywheel energy storage auxiliary thermal power unit AGC frequency modulation strategy is constructed;New AGC instruction is issued in advance under the scene of using thermal power unit model response AGC instruction, the proportional relationship between the output power of thermal power unit responding new AGC instruction and new AGC instruction is determined;AGC frequency modulation system cost-benefit model is constructed;AGC frequency modulation system cost-benefit model is solved, and the optimization proportional value corresponding to each AGC instruction stage is obtained, the application accurately controls the time of adding flywheel energy storage, and effectively reduces the influence of the combined power of the two on the whole frequency modulation system in advance entering the regulation dead zone.

Description

Technical Field

[0001] This application relates to the field of AGC frequency regulation technology, and in particular to a method, equipment and medium for AGC frequency regulation of flywheel energy storage auxiliary thermal power units. Background Technology

[0002] Automatic Generation Control (AGC) systems can detect frequency deviations in the power network in real time and convert them into corresponding active power deficits, rationally allocating them to the frequency-regulating power sources under their control, thereby adjusting their active power output accordingly. However, due to the low regulation accuracy and slow ramp-up rate of thermal power units, their real-time power response is weak, resulting in poor performance in tracking AGC commands. Furthermore, frequent load changes lead to severe wear and tear on generator equipment, seriously impacting the safety and economic efficiency of thermal power units. Flywheel energy storage, as a new type of energy storage system, features instantaneous response and a lifespan unaffected by state of charge, making it suitable for auxiliary units to participate in frequency regulation tasks.

[0003] Currently, most flywheel energy storage-assisted secondary frequency regulation strategies for thermal power units use changes in AGC power commands as the indicator of flywheel energy storage action, breaking down the thermal power unit's tracking of AGC power command changes into a tracking process under multiple AGC power commands. Based on performance indicators, the process of flywheel energy storage-assisted thermal power units tracking a single AGC power command is divided into a response phase, a ramp-up phase, and a maintenance phase. During the ramp-up phase, the addition of flywheel energy storage allows the assisted thermal power unit to respond to AGC power commands, thus advancing the time when the combined power output enters the regulation dead zone. While this rapid response improves the speed of the joint response of the thermal power unit and flywheel energy storage to AGC commands, it may also lead to the premature issuance of the next stage of AGC commands. In this case, if faced with continuously rising or falling AGC power command signals, the premature issuance of new AGC power commands would cause the thermal power unit to receive a newly issued AGC power command signal before completing the previous stage's AGC power command, forcing the thermal power unit to undertake a larger frequency regulation task. Summary of the Invention

[0004] The purpose of this application is to provide a flywheel energy storage-assisted AGC frequency regulation method, equipment and medium for thermal power units, which can accurately control the timing of adding flywheel energy storage and effectively reduce the impact of the combined power of the two entering the regulation dead zone in advance on the entire frequency regulation system.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a flywheel energy storage assisted AGC frequency regulation method for thermal power units, including the following steps.

[0007] Acquire raw data from the thermal power plant; the raw data includes raw AGC instruction data and actual power generation data; preprocess the raw AGC instruction data to obtain target AGC instruction data; segment the target AGC instruction data to obtain several AGC instruction stages; each AGC instruction stage consists of data in which the AGC instructions do not change within a continuous time period.

[0008] Construct a flywheel energy storage-assisted AGC frequency regulation strategy for thermal power units.

[0009] Construct a scenario for the early issuance of AGC instructions to obtain new AGC instructions corresponding to each AGC instruction stage.

[0010] By using a thermal power unit model to respond to the new AGC command, the proportional relationship between the output power of the thermal power unit in response to the new AGC command and the new AGC command itself is determined.

[0011] Construct a cost-benefit model for the AGC frequency modulation system; the cost-benefit model for the AGC frequency modulation system includes an objective function and constraints.

[0012] The cost-benefit model of the AGC frequency regulation system is solved to obtain the optimized ratio value corresponding to each AGC command stage; the optimized ratio value is the ratio of the output power of the thermal power unit after crossing the response dead zone under each AGC command to the AGC command.

[0013] Optionally, the preprocessing includes outlier handling and normalization.

[0014] Optionally, the expression for the objective function is as follows:

[0015] minC(k)=C ram (k)-B Kp (k);

[0016]

[0017] B Kp =K pd (k)×Y AGC ×D;

[0018]

[0019] Where C(k) is the objective function, C ram (k) represents the cost model for thermal power units, B Kp (k) represents the AGC frequency regulation mileage revenue model, n' is the total AGC command time length; χ is the unit ramp-up loss cost, and P... g (t,k) represents the output power at time t when the proportional value is k, P g(t-1,k) represents the output power at time t-1 when the proportion is k, Δt is the time interval, and K pd Y represents the regulatory performance index for the day. AGC D is the AGC compensation price; D is the daily adjustment depth. i Let be the depth of the i-th adjustment.

[0020] Optionally, the constraints include the flywheel energy storage system's reserve capacity constraint, SOC constraint, and flywheel energy storage system's discharge power constraint.

[0021] Optionally, the specific standby capacity constraints and SOC constraints of the flywheel energy storage system are as follows:

[0022] S min ≤S soc (t,k)≤S max ;

[0023] In the formula, S soc (t,k) represents the SOC of the energy storage system when the proportion is k at time t. max and D min These are the upper and lower limits of the State of Charge (SOC) of the energy storage system, respectively.

[0024] The specific discharge power constraints of the flywheel energy storage system are as follows:

[0025]

[0026] In the formula, P fd P is the maximum allowable discharge power of the system. fd,e The rated discharge power of the system; D low This represents a low SOC value for the energy storage system.

[0027] Optionally, the cost-benefit model of the AGC frequency modulation system is solved to obtain the optimized ratio value corresponding to each AGC instruction stage, specifically including:

[0028] The cost-benefit model of the AGC frequency modulation system is solved using the particle swarm optimization algorithm to obtain the optimized ratio value corresponding to each AGC instruction stage.

[0029] Optionally, the cost-benefit model of the AGC frequency modulation system is solved using the particle swarm optimization algorithm to obtain the optimized ratio value corresponding to each AGC command stage, specifically including:

[0030] Initialize population parameters; population parameters include population size, number of optimization variables, number of generations, range of optimization variables, forgetting factor, and learning factor;

[0031] Calculate the fitness value of each particle in the initial population; the fitness value of a particle is calculated using the objective function.

[0032] Determine the initial individual optimal position and the global optimal position;

[0033] Update the velocity and position of each particle;

[0034] Calculate the new fitness of each particle in the population, and determine the current optimal position of the individual and the current global optimal position;

[0035] Compare the current individual optimal position with the current global optimal position and the initial individual optimal position with the global optimal position. If the current individual optimal position is greater than the initial individual optimal position or the current global optimal position is greater than the initial global optimal position, then replace the initial individual optimal position with the current individual optimal position and the current global optimal position.

[0036] Determine if the set iteration termination condition has been met. If it is met, exit the loop and set the optimization variable corresponding to the current iteration number as the optimization ratio value corresponding to the AGC instruction stage. If it is not met, return to the "update the velocity and position of each particle" step.

[0037] Optionally, the iteration termination condition is set when the current iteration count reaches the maximum iteration count or the fitness value of the current global optimal position reaches a preset optimization accuracy.

[0038] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described flywheel energy storage assisted thermal power unit AGC frequency regulation method.

[0039] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described flywheel energy storage assisted thermal power unit AGC frequency regulation method.

[0040] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0041] This application provides a flywheel energy storage-assisted AGC frequency regulation method, equipment, and medium for thermal power units. By solving the optimized ratio value corresponding to each AGC command stage through the cost-benefit model of the AGC frequency regulation system, the timing of adding flywheel energy storage can be precisely controlled, effectively reducing the impact of the combined power of the two entering the regulation dead zone in advance on the entire frequency regulation system. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of a flywheel energy storage assisted AGC frequency regulation method for thermal power units according to an embodiment of this application;

[0044] Figure 2 A flowchart illustrating an AGC frequency regulation method for a flywheel energy storage assisted thermal power unit according to an embodiment of this application;

[0045] Figure 3 A schematic diagram illustrating the specific process of a flywheel energy storage assisted AGC frequency regulation method for thermal power units provided in an embodiment of this application;

[0046] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] The flywheel energy storage-assisted AGC frequency regulation method for thermal power units provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send raw data to server 104, including raw AGC command data and actual power generation data. After receiving the raw AGC command data, server 104 preprocesses the raw AGC command data to obtain target AGC command data. The target AGC command data is then segmented to obtain several AGC command stages. A flywheel energy storage-assisted AGC frequency regulation strategy for thermal power units is constructed. Using a thermal power unit model to respond to new AGC commands in advance, the proportional relationship between the output power of the thermal power unit responding to the new AGC command and the new AGC command is determined. A cost-benefit model for the AGC frequency regulation system is constructed. The cost-benefit model is solved to obtain the optimized proportional value corresponding to each AGC command stage. Server 104 can feed back the optimized ratio value corresponding to the AGC instruction stage for the thermal power plant to terminal 102. Furthermore, in some embodiments, the flywheel energy storage-assisted AGC frequency regulation method for thermal power units can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly perform AGC frequency regulation processing on the raw data, or server 104 can obtain the raw data from the data storage system and perform AGC frequency regulation processing on the raw data.

[0050] The terminal 102 can be, but is not limited to, various desktop computers and laptops. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0051] In one exemplary embodiment, such as Figure 2 and Figure 3 As shown, a flywheel energy storage-assisted AGC frequency regulation method for thermal power units is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 206. Wherein:

[0052] Step 201: Obtain raw data from the thermal power plant; the raw data includes raw AGC instruction data and actual power generation data; preprocess the raw AGC instruction data to obtain target AGC instruction data; segment the target AGC instruction data to obtain several AGC instruction stages; each AGC instruction stage consists of data in which the AGC instructions do not change within a continuous time period.

[0053] Step 202: Construct a flywheel energy storage-assisted AGC frequency regulation strategy for thermal power units.

[0054] Step 203: Construct a scenario for the early issuance of AGC instructions to obtain the new AGC instructions corresponding to each AGC instruction stage.

[0055] Step 204: Using the thermal power unit model to respond to the new AGC command, determine the ratio between the output power of the thermal power unit responding to the new AGC command and the new AGC command.

[0056] Step 205: Construct a cost-benefit model for the AGC frequency modulation system; the cost-benefit model for the AGC frequency modulation system includes an objective function and constraints.

[0057] Step 206: Solve the cost-benefit model of the AGC frequency regulation system to obtain the optimized ratio value corresponding to each AGC command stage; the optimized ratio value is the ratio of the output power of the thermal power unit after crossing the response dead zone under each AGC command to the AGC command.

[0058] Implementing steps 201 to 206 above, and targeting a flywheel energy storage-assisted AGC frequency regulation system for thermal power units in a typical scenario, with the goal of improving system profitability, and considering the power-type energy storage characteristics of the flywheel energy storage system, a control method is proposed for the segmented participation of flywheel energy storage in the AGC regulation of the assisted thermal power unit. Based on performance indicators and the real-time status of the unit, the flywheel energy storage assists the unit in AGC frequency regulation in stages. By optimizing the ratio of the output power of the thermal power unit after crossing the response dead zone under each AGC command to the AGC command, the timing of adding flywheel energy storage is precisely controlled, effectively reducing the impact of premature AGC command issuance caused by the combined power of the two entering the regulation dead zone prematurely on the entire frequency regulation system, significantly improving the profitability of the flywheel energy storage-assisted thermal power unit's AGC frequency regulation system in a typical scenario.

[0059] In another exemplary embodiment of this application, step 201 may include steps 301 to 304:

[0060] Step 301: Data Acquisition. Collect raw data from the thermal power plant. First, collect raw data from the distributed control system (DCS) of the generator units. Select one month from each quarter throughout the year as historical operating data for the units to avoid errors caused by seasonal or natural variations in electricity load. The data sampling interval is 1 second, ensuring that the thermal power units operate without shutdowns or faults within the data acquisition range. The collected data includes AGC commands (P). AGC Actual power output P g .

[0061] Preprocessing includes outlier handling and normalization to identify and correct outliers and missing values ​​in the data. The specific processes are shown in steps 302 and 303, respectively.

[0062] Step 302: Outlier Handling. Outlier data collected in Step 301 are processed using the mean square method. The formula for calculating the mean square method is shown in Equation (1).

[0063]

[0064] In the formula, N is the number of samples, x i Let x be the value of the i-th sample, μ be the sample mean, and δ be the sample variance. If |x i -μ|>3δ, then x i If an outlier is identified, a linear fitting method is used to replace it.

[0065] Step 303: Normalization. To accelerate the training speed of the secondary frequency modulation network model, the data collected in step 301 is normalized. The values ​​are converted to the range [0,1]. The normalization process is shown in equation (2).

[0066]

[0067] In the formula, x min x is the minimum value among the processed feature data. max It represents the maximum value among the processed feature data.

[0068] The target AGC instruction data can be obtained through steps 302 and 303.

[0069] Step 304: Segment the processed target AGC instruction data. Select n consecutive seconds of AGC power instruction that rises throughout the day, P AGC =P AGC (1),P AGC (2),…,P AGC (n)(n represents the time information, indicating the total duration of the AGC instruction, P) AGC (1)≤P AGC (2)≤…≤PAGC (n)), which will be P that does not change in continuous time. AGC This is recorded as an AGC instruction, i.e., P AGC i = [P AGC (a i ),P AGC (a i +1),…,P AGC (b i (where i represents the AGC stage information, assuming the AGC instruction is divided into D segments, a) i b i The time information is represented by P, which is the start and end time of the i-th AGC instruction stage, respectively. AGC (a i ) = P AGC (a i +1)=…=P AGC (b i ), P AGC (a i ) is a i AGC instruction at time, P AGC (a i +1) is a i The AGC instruction at time +1, P AGC (b i ) for b i (AGC instructions at the moment).

[0070] The specific steps for constructing the flywheel energy storage-assisted AGC frequency regulation strategy for thermal power units in step 202 above are as follows:

[0071] Within the i-th AGC instruction phase:

[0072] (1) When P g (t)≤P AGC (i-1)+P x hour:

[0073] P f_ref (t)=P AGC (i-1)+P x -P g (t) (3);

[0074] In the formula, P f_ref (t) is the power setting for flywheel output; P AGC (i-1) represents the AGC instruction for the (i-1)th stage; P x The response dead zone is 1% of the rated power of the thermal power unit; t represents time information, a i ≤t≤a i +t i The unit is seconds.

[0075] (2) When P g (t)>P AGC (i-1)+P x hour:

[0076]

[0077] In the formula, P t To adjust the dead zone, it is 0.5% of the rated power of the thermal power unit; t f,i When flywheel energy storage is added in stage i, t f,i ≥t, and P g (t f,i )=k×P AGCi P g (t f,i ) for t f,i denoted as , and k is the ratio of the output power of the thermal power unit after it crosses the response dead zone under the AGC command to the output power of the AGC command.

[0078] In step 203 above, during the ramp-up phase, the addition of flywheel energy storage causes the combined power output of the flywheel and the thermal power unit to enter the regulation dead zone earlier, potentially leading to the earlier issuance of the next stage AGC command. During the i-th AGC command phase, as determined in step 202, the flywheel energy storage... f,i It immediately joins and begins to output power. Based on its fast response speed and precise tracking of load changes, this application assumes that the flywheel energy storage reaches its power setpoint 1 second after it begins to respond, that is, within the i-th AGC command stage, (t f,i +1) When the system output reaches the current stage of the AGC adjustment dead zone, (t) f,i +2) A new AGC instruction is issued at this time, at which point P AGCi =[P AGC (a i ),P AGC (a i +1),…,P AGC (t f,i +1)],t f,i +1≤b i a i+1 =t f,i +2.

[0079] In step 204, the existing thermal power unit model is used to respond to the new AGC command. Let P' be the output power of the thermal power unit responding to the new AGC command. g As can be seen from step 202, P' g =f(k).

[0080] The modeling process of the thermal power unit model is as follows: Feedwater pressure, feedwater flow rate, and separator pressure parameters are simplified and calculated based on the feedwater pump speed; the steam drum / separator water level is simplified to the integral of the difference between the inlet and outlet working fluid flow rates; the steam flow rate is determined by calculating the working fluid dryness using the mass and energy conservation methods; the temperature is calculated from pressure and enthalpy using the integral function of the PID module, and then the main steam temperature is simulated and calculated using the mass and energy conservation principles; the flue gas volume and induced draft fan suction volume are simulated and calculated, and the difference between the two is integrated, then the inlet deviation is changed to simplify the simulation of the furnace negative pressure; secondary air pressure and primary air pressure are simulated and calculated using a similar method to the flue gas volume and induced draft fan suction volume; the boiler fuel quantity is simulated and calculated based on the coal feeder speed; the unit load is simulated and calculated based on the fuel quantity or main steam flow rate; a simplified model of the controlled object is established within the DCS system using DCS configuration tools, thereby achieving a control closed loop. That is, after establishing a simulation model within the thermal power unit's distributed control system (DCS), a control closed loop is achieved.

[0081] The following section describes the construction of a cost-benefit model for a flywheel energy storage-assisted AGC frequency regulation system for thermal power units, and the optimization of the AGC frequency regulation system cost-benefit model.

[0082] (1) Construct a cost model for thermal power units.

[0083]

[0084] In the formula: C ram (k) represents the cost model for thermal power units, n' represents the total AGC command time length, χ represents the unit ramp-up loss cost, and P g (t,k) represents the output power at time t when the proportional value is k, P g (t-1,k) represents the output power at time t-1 when the proportion is k, and Δt is the time interval.

[0085] (2) Construct an AGC frequency regulation mileage revenue model. To address the economic losses incurred by thermal power plants participating in AGC, the market will provide certain AGC economic compensation to AGC units. The calculation formula for the daily compensation fee for AGC service contribution, i.e., the AGC frequency regulation mileage revenue model, is shown in equation (6).

[0086] B Kp (k)=K pd (k)×Y AGC ×D (6).

[0087] Among them, B Kp (k) represents the AGC frequency modulation mileage revenue model, K pd Y represents the regulatory performance index for the day. AGCD is the AGC compensation price; D is the daily adjustment depth, which is the sum of the daily adjustment amounts, as shown in equation (7).

[0088]

[0089] In the formula, D i For the i-th adjustment of depth, if the system output reaches the command value when receiving the next AGC command, then D i =|P AGCi -P si |; If the system output does not reach the command value when receiving the next AGC command, D i =|P Ei -P Si |;P Si P represents the system output at the start of the i-th AGC adjustment. Ei This represents the system output at the end of the i-th AGC adjustment.

[0090] (3) Optimize the cost-benefit model of the flywheel energy storage auxiliary thermal power unit AGC frequency regulation system.

[0091] The expression for the objective function is defined as shown in equation (8).

[0092] minC(k)=C ram (k)-B Kp (k) (8).

[0093] Where C(k) is the objective function.

[0094] The constraints include the reserve capacity constraint and SOC constraint of the flywheel energy storage system, and the discharge power constraint of the flywheel energy storage system.

[0095] The specific constraints on the standby capacity and SOC of the flywheel energy storage system are shown in Equation (9).

[0096] S min ≤S soc (t,k)≤S max (9).

[0097] In the formula, S soc (t,k) represents the SOC of the energy storage system at time t when the proportion is k; max and S min These are the upper and lower limits of the SOC of the energy storage system, which can be taken as 0.8 and 0.2 respectively.

[0098] The discharge power constraint of the flywheel energy storage system is shown in Equation (10).

[0099]

[0100] In the formula, Pfd P is the maximum allowable discharge power of the system. fd,e The rated discharge power of the system; S low For energy storage systems, a low SOC value of 0.4 is acceptable.

[0101] In another exemplary embodiment of this application, step 206 specifically includes: using a particle swarm optimization algorithm to solve the cost-benefit model of the AGC frequency modulation system to obtain the optimized ratio value corresponding to each AGC command stage. This may specifically include steps 401 to 407.

[0102] Step 401: Initialize population parameters; population parameters include population size m, number of optimization variables N, number of generations t, range of optimization variable X, forgetting factor ω, learning factor, etc. The optimization variable is the proportion value k.

[0103] Step 402: Calculate the fitness value of each particle in the initial population; the fitness value of a particle is calculated using the objective function. The fitness Q of the initial population is expressed in the form shown in Equation (11).

[0104] Q = C ram (k)-B Kp (k) (11).

[0105] Step 403: Determine the initial optimal position k of the individual. bestin,i and the global optimal position k bestgn,i .

[0106] Step 404: Measure the velocity v of each particle. in and position k i Update the velocity and position as shown in formulas (12) and (13), respectively.

[0107] v in =ωv (i-1)n +c1r1[k bestin,i-1 -k i-1 ]+c2r2[k bestgn,i-1 -k i-1 (12).

[0108] k i =k i-1 +v in (13).

[0109] In the formula, c1 and c2 are learning factors, which can take the value 2; r1 and r2 are random numbers between (0,1); v (i-1)n Let k be the velocity of the particle during the (i-1)th update. bestin,i-1 Let k be the optimal position of the individual corresponding to the (i-1)th iteration. bestgn,i-1k represents the globally optimal position corresponding to the (i-1)th iteration. i-1 This represents the particle position corresponding to the (i-1)th iteration.

[0110] Step 405: Calculate the new fitness of each particle in the population, and determine the current optimal position of the individual and the current global optimal position.

[0111] Step 406: Compare k bestin and k bestgn If the value is superior, it will be replaced. Specifically: compare the current individual optimal position with the current global optimal position and the initial individual optimal position with the global optimal position. If the current individual optimal position is greater than the initial individual optimal position or the current global optimal position is superior to the initial global optimal position, then the initial individual optimal position and global optimal position will be replaced with the current individual optimal position and the current global optimal position.

[0112] Step 407: Determine if the set iteration termination condition has been met. If it is met, exit the loop and set the optimization variable corresponding to the current iteration number as the optimization ratio value corresponding to the AGC instruction stage. If it is not met, return to step 404. The iteration termination condition is set as either the current iteration number reaching the maximum iteration number or the fitness value of the current global optimal position reaching the preset optimization accuracy.

[0113] After iteration, the optimized variable k is obtained, such that the objective function C(k) = C ram (k)-B Kp The optimal solution is achieved when (k) is minimized.

[0114] This application also provides an application scenario in which the above-mentioned flywheel energy storage-assisted thermal power unit AGC frequency regulation method is applied. Specifically, the flywheel energy storage-assisted thermal power unit AGC frequency regulation method provided in this embodiment can be applied in an AGC frequency regulation scenario. The AGC frequency regulation scenario includes a data acquisition stage and an AGC frequency regulation link; the original AGC command data enters the AGC frequency regulation link from the data acquisition stage to obtain the optimized ratio value corresponding to the corresponding AGC command stage. The flywheel energy storage-assisted thermal power unit AGC frequency regulation method provided in this embodiment belongs to the AGC frequency regulation link. Specifically, in the AGC frequency regulation link process targeting the original AGC command data, the original AGC command data can be preprocessed to obtain the target AGC command data. The target AGC command data can then be segmented to obtain several AGC command stages. A flywheel energy storage-assisted thermal power unit AGC frequency regulation strategy can be constructed. By utilizing the thermal power unit model to respond to the new AGC command in the scenario of early AGC command issuance, the proportional relationship between the output power of the thermal power unit in response to the new AGC command and the new AGC command can be determined, and an AGC frequency regulation system cost-benefit model can be constructed. The AGC frequency regulation system cost-benefit model can then be solved to obtain the optimized proportional value corresponding to each AGC command stage.

[0115] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores AGC frequency regulation data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a flywheel energy storage-assisted AGC frequency regulation method for thermal power units.

[0116] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0117] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0118] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0120] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0121] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for frequency regulation of flywheel energy storage-assisted thermal power units using AGC, characterized in that, The flywheel energy storage assisted thermal power unit AGC frequency regulation method includes: Acquire raw data from the thermal power plant; the raw data includes raw AGC instruction data and actual power generation data; preprocess the raw AGC instruction data to obtain target AGC instruction data; segment the target AGC instruction data to obtain several AGC instruction stages; each AGC instruction stage consists of data in which the AGC instructions do not change within a continuous time period; The flywheel energy storage-assisted AGC frequency regulation strategy for thermal power units is constructed as follows: In the i During the AGC instruction phase: when hour: ； In the formula, This is the actual power output; Set the power output for the flywheel; For the first i AGC instructions in stage -1; In response to dead zones; t For time information; when hour: ； In the formula, To adjust the dead zone; For the stage i When adding flywheel energy storage, ,and , for The output power at any given moment. This is the ratio of the output power of the thermal power unit after it crosses the response dead zone under the AGC command to the AGC command itself. Construct a scenario for the early issuance of AGC instructions to obtain new AGC instructions corresponding to each AGC instruction stage; By using a thermal power unit model to respond to the new AGC command, the functional relationship between the output power of the thermal power unit in response to the new AGC command and the proportional value is determined. Construct a cost-benefit model for the AGC frequency modulation system; the cost-benefit model for the AGC frequency modulation system includes an objective function and constraints. The cost-benefit model of the AGC frequency regulation system is solved to obtain the optimized ratio value corresponding to each AGC command stage; the optimized ratio value is the ratio of the output power of the thermal power unit after crossing the response dead zone under each AGC command to the AGC command.

2. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 1, characterized in that, The preprocessing includes outlier handling and normalization.

3. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 1, characterized in that, The expression for the objective function is as follows: ； ； ； ； in, Let be the objective function. For the cost model of thermal power units, This is the AGC frequency modulation mileage revenue model. The total duration of the AGC instruction; χ Costs associated with generator ramp-up losses. for The time scale value is Output power at that time for The time scale value is Output power at that time For time intervals, The performance indicators for regulation on that day; The price is a compensation for AGC. Adjust the depth daily. For the first Secondary adjustment depth.

4. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 1, characterized in that, The constraints include the spare capacity constraint and SOC constraint of the flywheel energy storage system, and the discharge power constraint of the flywheel energy storage system.

5. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 4, characterized in that, The specific standby capacity constraints and SOC constraints of the flywheel energy storage system are as follows: ； In the formula, for The time scale value is The energy storage system SOC at that time and These are the upper and lower limits of the State of Charge (SOC) of the energy storage system, respectively. The specific discharge power constraints of the flywheel energy storage system are as follows: ； In the formula, This represents the maximum allowable discharge power of the system. The rated discharge power of the system; This represents a low SOC value for the energy storage system.

6. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 1, characterized in that, Solving the cost-benefit model of the AGC frequency modulation system yields the optimized ratio value corresponding to each AGC instruction stage, specifically including: The cost-benefit model of the AGC frequency modulation system is solved using the particle swarm optimization algorithm to obtain the optimized ratio value corresponding to each AGC instruction stage.

7. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 6, characterized in that, The cost-benefit model of the AGC frequency modulation system is solved using the particle swarm optimization algorithm to obtain the optimized ratio value corresponding to each AGC command stage, specifically including: Initialize population parameters; population parameters include population size, number of optimization variables, number of generations, range of optimization variables, forgetting factor, and learning factor; Calculate the fitness value of each particle in the initial population; the fitness value of a particle is calculated using the objective function. Determine the initial individual optimal position and the global optimal position; Update the velocity and position of each particle; Calculate the new fitness of each particle in the population, and determine the current optimal position of the individual and the current global optimal position; Compare the current individual optimal position with the current global optimal position and the initial individual optimal position with the global optimal position. If the current individual optimal position is greater than the initial individual optimal position or the current global optimal position is greater than the initial global optimal position, then replace the initial individual optimal position with the current individual optimal position and the current global optimal position. Determine if the set iteration termination condition has been met. If it is met, exit the loop and set the optimization variable corresponding to the current iteration number as the optimization ratio value corresponding to the AGC instruction stage. If it is not met, return to the "update the velocity and position of each particle" step.

8. The flywheel energy storage-assisted AGC frequency regulation method for thermal power units according to claim 7, characterized in that, The iteration termination condition is set when the current iteration count reaches the maximum iteration count or the fitness value of the current global optimal position reaches the preset optimization accuracy.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the flywheel energy storage-assisted thermal power unit AGC frequency regulation method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the AGC frequency regulation method for flywheel energy storage assisted thermal power units as described in any one of claims 1-8.

Images