Flywheel energy storage-based thermal power generating unit frequency modulation optimization method and device

Abstract

The invention relates to the technical field of power system frequency modulation, and discloses a thermal power generating unit frequency modulation optimization method and device based on flywheel energy storage, and the method comprises the steps: receiving an AGC frequency modulation instruction transmitted by a dispatching center in real time, and calculating a target power variation value between the AGC frequency modulation instruction and the power of a thermal power generating unit; according to the operation state of the flywheel energy storage system and a thermal power generating unit power change response model, using an improved ant colony algorithm to decompose the target power variation value to obtain a power distribution scheme; a power instruction is issued to the thermal power generating unit and the flywheel energy storage system according to the power distribution scheme, and the thermal power generating unit and the flywheel energy storage system operate according to the distributed power instruction. Cooperative power distribution of the flywheel and the thermal power generating unit is achieved through the improved ant colony algorithm, cooperative frequency modulation control is achieved, the flywheel energy storage system is used for rapidly complementing the difference value between the thermal power generating unit and an AGC instruction, thermal power response lag is made up, and the frequency response efficiency of the whole system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency regulation, and in particular to a method and device for optimizing frequency regulation of a thermal power unit based on flywheel energy storage. Background Art

[0002] With the rapid growth of renewable energy, power grids are placing higher demands on the speed and precision of frequency regulation. Traditional thermal power units, due to their high inertia and slow response speed, are unable to quickly respond to AGC (Automatic Generation Control) signals when system load fluctuates dramatically, resulting in frequency lag and unstable control.

[0003] Flywheel energy storage systems, with their high power density and millisecond-level response speeds, offer significant advantages in frequency regulation scenarios within short timescales. However, most current solutions utilize flywheels solely as auxiliary energy storage, lacking in-depth research on their coordinated control with thermal power units. As a result, the overall frequency regulation performance of the systems still struggles to meet the demands of high-penetration renewable energy systems. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for optimizing the frequency regulation of thermal power units based on flywheel energy storage, in order to solve the problem that most current solutions only use the flywheel as an auxiliary energy storage and operate independently, lack in-depth research on its coordinated control with the thermal power units, and the overall frequency regulation performance of the system is difficult to meet the needs of high-penetration new energy systems.

[0005] In a first aspect, the present invention provides a method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage, the method comprising: Receive the AGC frequency modulation command issued by the dispatch center in real time, and calculate the difference between the AGC frequency modulation command and the target power variation of the thermal power unit; According to the operating status of the flywheel energy storage system and the power change response model of the thermal power unit, the target power variation value is decomposed using an improved ant colony algorithm to obtain a power allocation plan; According to the power allocation scheme, power instructions are sent to the thermal power generation units and the flywheel energy storage system, and the thermal power generation units and the flywheel energy storage system operate according to the allocated power instructions.

[0006] The present invention provides a method for optimizing the frequency regulation of a thermal power unit based on flywheel energy storage. This method realizes the coordinated power distribution between the flywheel and the thermal power unit through an improved ant colony algorithm, realizes coordinated frequency regulation control, and utilizes the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, thereby compensating for the delayed thermal power response and improving the overall system frequency response efficiency.

[0007] In an optional embodiment, based on the energy state parameters of the flywheel energy storage system and the power variation response model of the thermal power unit, an improved ant colony algorithm is used to decompose the target power variation value to obtain a power allocation scheme, including: Initialize the search space and control parameters required for optimization; Based on the actual operating characteristics of the flywheel energy storage system and the thermal power unit, a power allocation path space is constructed, and the probability of selecting each power allocation path is calculated based on the pheromone function and the heuristic function. Obtaining real-time operating status parameters of the flywheel energy storage system and the thermal power unit, and dynamically adjusting the pheromone function and the heuristic function according to the operating status parameters; When the algorithm falls into a local optimum, the chaotic perturbation mechanism is triggered to perform nonlinear perturbations on the power allocation parameters of a preset part of the ant colony individuals and regenerate the power allocation parameters; For a power allocation scheme whose score is lower than the average value after a preset number of consecutive iterations, reinitialize the power allocation parameters; Utilize multiple processing threads to perform power path search and objective function evaluation simultaneously; When the set maximum number of iterations is reached, or the objective function value of the optimal solution meets the frequency modulation error tolerance condition, the optimal power allocation scheme in the current iteration round is output.

[0008] By introducing an intelligent optimization algorithm under multiple constraints, the stability and robustness of the power allocation strategy are improved.

[0009] In an optional embodiment, multiple processing threads are used to simultaneously perform power path search and objective function evaluation, including: The power allocation scheme is decomposed into multiple subtasks and the power path search is performed in parallel by multiple processing threads; After each round of search, all path results are uniformly evaluated and pheromones are updated based on the evaluation results.

[0010] In an optional embodiment, updating pheromones according to the evaluation results includes: The power allocation path is determined to be a good path or a poor path based on the evaluation results; When the power allocation path is a good path, increasing the pheromone of the power allocation path; When the power allocation path is a poor quality path, the pheromone of the power allocation path is reduced.

[0011] In an optional embodiment, based on the operating state of the flywheel energy storage system and the power variation response model of the thermal power unit, an improved ant colony algorithm is used to decompose the target power variation value to obtain a power allocation plan, further comprising: During the path search process, an adaptive elimination mechanism is set up to automatically exclude paths that do not meet the flywheel energy safety threshold or thermal power ramp limit.

[0012] In an optional embodiment, the method further includes: Obtaining an energy state parameter of the flywheel energy storage system, and comparing the energy state parameter with a set threshold; When the energy state parameter is not within the set range, the flywheel energy storage system is not allowed to participate in frequency regulation; When the energy state parameter is within the set range, the flywheel energy storage system is allowed to participate in the frequency regulation task.

[0013] In an optional embodiment, the method further includes: The instructions of the thermal power units are ramped and the upper limit of the maximum change rate is set.

[0014] In a second aspect, the present invention provides a frequency regulation optimization device for a thermal power unit based on flywheel energy storage, the device comprising: The calculation module is used to receive the AGC frequency modulation instruction issued by the dispatching center in real time and calculate the difference between the AGC frequency modulation instruction and the target power variation value of the thermal power unit power; a decomposition module, configured to decompose the target power variation value using an improved ant colony algorithm according to the operating state of the flywheel energy storage system and the power variation response model of the thermal power unit to obtain a power allocation plan; The distribution module is used to send power instructions to the thermal power generation unit and the flywheel energy storage system according to the power distribution plan, and the thermal power generation unit and the flywheel energy storage system operate according to the distributed power instructions.

[0015] The present invention provides a frequency regulation optimization device for a thermal power unit based on flywheel energy storage. This device realizes coordinated power distribution between the flywheel and the thermal power unit through an improved ant colony algorithm, realizes coordinated frequency regulation control, and utilizes the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, thereby compensating for the delayed thermal power response and improving the overall system frequency response efficiency.

[0016] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the frequency regulation optimization method for a thermal power unit based on flywheel energy storage according to the above-mentioned first aspect or any corresponding embodiment thereof.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the frequency regulation optimization method for a thermal power unit based on flywheel energy storage according to the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 1 is a flow chart of a method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to an embodiment of the present invention; Figure 2 2 is a structural block diagram of a frequency regulation optimization device for a thermal power unit based on flywheel energy storage according to an embodiment of the present invention; Figure 3 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0021] The embodiment of the present invention provides a method for optimizing the frequency regulation of a thermal power unit based on flywheel energy storage, which decomposes the AGC instructions by introducing an improved ant colony algorithm to achieve dynamic coordinated response between the flywheel energy storage system and the thermal power unit.

[0022] According to an embodiment of the present invention, an embodiment of a method for optimizing the frequency regulation of a thermal power unit based on flywheel energy storage is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] In this embodiment, a method for optimizing the frequency regulation of a thermal power unit based on flywheel energy storage is provided. Figure 1 FIG. 1 is a flow chart of a method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S1: receiving the AGC frequency modulation instruction issued by the dispatching center in real time, and calculating the difference between the AGC frequency modulation instruction and the target power variation value of the thermal power unit power.

[0024] Specifically, AGC (Automatic Generation Control) frequency regulation commands are real-time power adjustment commands generated by the grid dispatching center based on parameters such as system frequency deviation and load fluctuation. They are used to control the output of generators and achieve dynamic grid frequency balance. Based on the received AGC commands, the power system combines the operating status and frequency regulation characteristics of the thermal power units to calculate the overall target power variation value ΔP, which serves as the basis for subsequent power adjustment and allocation.

[0025] Step S2: According to the operating status of the flywheel energy storage system and the power variation response model of the thermal power unit, the target power variation value is decomposed using the improved ant colony algorithm to obtain a power allocation plan.

[0026] Specifically, step S2 includes the following steps: Step S21 : Initializing the search space and control parameters required for optimization.

[0027] In this embodiment of the present invention, during the initialization phase, the thermal power unit receives an AGC instruction from the dispatcher and begins preparing for secondary frequency regulation. The algorithm system then initializes the search space and control parameters required for optimization, including the ant colony size (indicating the number of candidate power allocation solutions), pheromone weights (indicating the strength of guidance based on historical experience), heuristic weights (indicating the ability to guide current status, such as SOC adequacy and unit response delay), and the maximum number of iterations (to ensure the algorithm completes within the specified time limit). This phase establishes the basic optimization framework that meets the time constraints of real-time scheduling.

[0028] Step S22: constructing a power allocation path space based on the actual operating characteristics of the flywheel energy storage system and the thermal power unit, and calculating the probability of selecting each power allocation path based on the pheromone function and the heuristic function.

[0029] In an embodiment of the present invention, each ant represents a power allocation scheme, and the path selection is affected by state variables such as flywheel SOC, grid frequency deviation, and unit load margin. The probability of an individual selecting a path depends on two types of functions: the pheromone function reflects the accumulated experience of historical excellent strategies; the heuristic function is dynamically adjusted according to the current system state. For example, the closer the flywheel SOC is to the boundary, the lower the weight it is assigned, and the weight of its response capability increases when thermal power is in a low load range. In order to prevent the emergence of "infeasible solutions" that do not meet the actual operating restrictions in the strategy space, the present invention introduces an "adaptive elimination mechanism". During the path search process, an adaptive elimination mechanism is set to automatically exclude paths that do not meet the flywheel energy safety threshold or thermal power ramp limit, effectively avoiding the risk of frequency regulation failure or equipment overload.

[0030] Step S23: obtaining real-time operating status parameters of the flywheel energy storage system and the thermal power unit, and dynamically adjusting the pheromone function and the heuristic function according to the operating status parameters.

[0031] In this embodiment of the present invention, to adapt to dynamic changes in grid frequency, fluctuations in flywheel energy status, and changes in thermal power regulation capacity, the algorithm continuously obtains system feedback information (such as the degree of frequency offset, flywheel power curve response lag, and thermal power plant temperature rise) during operation and dynamically adjusts pheromones and heuristic functions. This strategy enables the algorithm to quickly re-optimize even when the system environment changes suddenly, making it suitable for scheduling scenarios with frequent frequency regulation commands.

[0032] Step S24: When the algorithm falls into a local optimum, the chaotic perturbation mechanism is triggered to perform nonlinear perturbations on the power allocation parameters of a preset part of the ant colony individuals to regenerate the power allocation parameters.

[0033] In an embodiment of the present invention, in some AGC instruction scenarios, the system frequency deviation is small and the objective function changes slightly, and the traditional ant colony search is prone to fall into local optimality or even stagnation. To solve this problem, the present application designs a chaotic perturbation trigger mechanism: in multiple consecutive rounds of iterations, if the improvement value of the optimal objective function is lower than the set threshold, the system determines that it is currently trapped in a local minimum interval; at this time, the chaotic perturbation mechanism is triggered, and the positions of some preset ant colony individuals are perturbed in a nonlinear sequence perturbation manner (such as Logistic mapping) to expand the search range. Among them, the preset part of the ant colony individuals are the ants with the bottom 30% ranking, which is only used as an example and is not limited to this. This "global jump perturbation" mechanism effectively avoids the risk of missing the global optimal solution due to the early local optimal trap, which is extremely critical in multi-constrained non-convex problems such as power system frequency regulation.

[0034] Step S25 : reinitialize the power allocation parameters for the power allocation schemes whose scores are lower than the average value after a preset number of consecutive iterations.

[0035] In this embodiment of the present invention, after the chaotic perturbation is executed, some individuals may still be unable to escape the inferior solution set, especially during the system's high-frequency scheduling phase when the flywheel SOC continues to decrease and the thermal power response boundary is compressed. Therefore, the algorithm establishes an individual "inferior solution elimination mechanism" to reinitialize ants that have poor scores for a preset number of consecutive iterations and assign them a new strategy combination. The preset number of rounds is 15, which is only used as an example and is not limited to this. This operation improves population diversity, prevents premature population convergence, and enhances search space coverage.

[0036] Step S26 : Utilize multiple processing threads to simultaneously execute power path search and objective function evaluation.

[0037] In this embodiment of the present invention, considering that the power system frequency regulation response window is typically controlled within 2 seconds, this algorithm supports a parallel path search mechanism to enhance real-time computing capabilities. This mechanism breaks down the power allocation solution into multiple subtasks, and executes the power path search in parallel across multiple processing threads. These multiple processing threads simultaneously perform the power path search and objective function evaluation, significantly reducing the time required for a single iteration.

[0038] After each search round, all path results are evaluated and pheromones are updated based on the evaluation results. The power allocation path is determined to be either a good or bad path based on the evaluation results. If a path is a good path, the pheromone level is increased; if it is a bad path, the pheromone level is decreased. The evaluation results include comprehensive indicators such as frequency modulation error, power utilization, and flywheel life factor. If a path is determined to be a good path, it receives positive feedback, increasing its probability of subsequent path selection. If a path is determined to be a bad path, the pheromone level of the bad path is automatically attenuated, and the path is gradually eliminated.

[0039] Step S27: When the set maximum number of iterations is reached, or the objective function value of the optimal solution meets the frequency modulation error tolerance condition, the optimal power allocation solution in the current iteration round is output.

[0040] In this embodiment of the present invention, the system determines whether the current round of power allocation has met termination criteria, including whether the adjustment error is within the tolerance range and whether the maximum number of iterations has been reached. If these criteria are not met, the allocation plan is readjusted and executed in a loop. If these criteria are met, the current adjustment process ends and the optimal power allocation strategy for the current iteration is output. Specifically, these criteria include: the response power value and direction (charging / discharging) of the flywheel energy storage system; the adjustment power slope and target output value of the thermal power unit; the time ratio of the two executions and the load distribution coefficient. This result serves as the execution input of the control system, directly driving the operation of the flywheel converter and the thermal power regulation mechanism.

[0041] The core advantage of this algorithm lies in its combination of swarm intelligence optimization (ant colony algorithm), a chaotic global jump mechanism, and a dynamic environment-aware adjustment strategy. This effectively improves the efficiency and convergence accuracy of control strategy parameter optimization, making it particularly suitable for complex multivariable scenarios such as power system frequency regulation, energy management system optimization, and dispatch strategy search. Furthermore, by introducing a multi-constrained intelligent optimization algorithm, the stability and robustness of the power allocation strategy are enhanced.

[0042] Step S3: sending power instructions to the thermal power generation unit and the flywheel energy storage system according to the power allocation plan. The thermal power generation unit and the flywheel energy storage system operate according to the allocated power instructions.

[0043] Specifically, after calculating the optimal power allocation plan using an improved ant colony algorithm, two independent power commands are generated: the flywheel energy storage system command and the thermal power unit command. Upon receiving the command, the flywheel energy storage system rapidly adjusts the motor speed through the converter to quickly compensate for the difference between the thermal power unit command and the AGC command, achieving millisecond-level frequency regulation and compensating for the thermal power response lag. Upon receiving the command, the thermal power unit ramps the command and sets a maximum rate of change upper limit. The boiler and turbine control mechanisms gradually adjust the output according to the ramp rate, avoiding mechanical wear caused by step commands and reducing life loss. This algorithm aims to achieve collaborative operation between the thermal power unit and the flywheel energy storage system in an automatic generation control (AGC) scenario, while balancing system stability, response speed, and energy sustainability.

[0044] The present invention provides a method for optimizing the frequency regulation of a thermal power unit based on flywheel energy storage. This method realizes the coordinated power distribution between the flywheel and the thermal power unit through an improved ant colony algorithm, realizes coordinated frequency regulation control, and utilizes the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, thereby compensating for the delayed thermal power response and improving the overall system frequency response efficiency.

[0045] In an optional implementation, before executing step S21, the method further includes: Step S201: Obtain energy state parameters of the flywheel energy storage system, and compare the energy state parameters with a set threshold.

[0046] Step S202: When the energy state parameter is not within the set range, the flywheel energy storage system is not allowed to participate in frequency regulation.

[0047] Step S203: When the energy state parameter is within the set range, the flywheel energy storage system is allowed to participate in the frequency regulation task.

[0048] Specifically, the flywheel energy storage system's energy state parameter, namely its current state of charge (SOC), is determined based on the SOC value and the set upper and lower thresholds. If the SOC value is below the lower threshold, it indicates insufficient flywheel energy storage, and the flywheel system is temporarily not used in the current regulation cycle. If the SOC value is within the set range, the flywheel system is allowed to participate in the frequency regulation task. The setting range is [lower threshold, upper threshold].

[0049] In this embodiment, a frequency regulation optimization device for a thermal power unit based on flywheel energy storage is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments, and the details that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0050] This embodiment provides a frequency regulation optimization device for a thermal power unit based on flywheel energy storage, such as Figure 2 Shown, including: The calculation module 21 is used to receive the AGC frequency modulation instruction issued by the dispatching center in real time, and calculate the difference between the AGC frequency modulation instruction and the target power variation value of the thermal power unit power.

[0051] The decomposition module 22 is used to decompose the target power variation value using the improved ant colony algorithm according to the energy state parameters of the flywheel energy storage system and the power variation response model of the thermal power unit to obtain a power allocation plan.

[0052] The distribution module 23 is used to send power instructions to the thermal power generation units and the flywheel energy storage system according to the power distribution plan, and the thermal power generation units and the flywheel energy storage system operate according to the distributed power instructions.

[0053] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0054] The flywheel energy storage-based frequency regulation optimization device for thermal power units in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0055] The present invention provides a frequency regulation optimization device for a thermal power unit based on flywheel energy storage. This device realizes coordinated power distribution between the flywheel and the thermal power unit through an improved ant colony algorithm, realizes coordinated frequency regulation control, and utilizes the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, thereby compensating for the delayed thermal power response and improving the overall system frequency response efficiency.

[0056] The embodiment of the present invention also provides a computer device having the above Figure 2 The frequency regulation optimization device of a thermal power unit based on flywheel energy storage is shown.

[0057] See also Figure 3 , Figure 3 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 3As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0058] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0059] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0060] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0061] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0062] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0063] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0064] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage, characterized in that: The method comprises: Receive the AGC frequency modulation command issued by the dispatch center in real time, and calculate the difference between the AGC frequency modulation command and the target power variation of the thermal power unit; According to the operating status of the flywheel energy storage system and the power change response model of the thermal power unit, the target power variation value is decomposed using an improved ant colony algorithm to obtain a power allocation plan; According to the power allocation scheme, power instructions are sent to the thermal power generation units and the flywheel energy storage system, and the thermal power generation units and the flywheel energy storage system operate according to the allocated power instructions.

2. The method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to claim 1, characterized in that: According to the operating status of the flywheel energy storage system and the power change response model of the thermal power unit, the target power variation value is decomposed using the improved ant colony algorithm to obtain a power allocation plan, including: Initialize the search space and control parameters required for optimization; Based on the actual operating characteristics of the flywheel energy storage system and the thermal power unit, a power allocation path space is constructed, and the probability of selecting each power allocation path is calculated based on the pheromone function and the heuristic function. Obtaining real-time operating status parameters of the flywheel energy storage system and the thermal power unit, and dynamically adjusting the pheromone function and the heuristic function according to the operating status parameters; When the algorithm falls into a local optimum, the chaotic perturbation mechanism is triggered to perform nonlinear perturbations on the power allocation parameters of a preset part of the ant colony individuals and regenerate the power allocation parameters; For a power allocation scheme whose score is lower than the average value after a preset number of consecutive iterations, reinitialize the power allocation parameters; Utilize multiple processing threads to perform power path search and objective function evaluation simultaneously; When the set maximum number of iterations is reached, or the objective function value of the optimal solution meets the frequency modulation error tolerance condition, the optimal power allocation scheme in the current iteration round is output.

3. The method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to claim 2, characterized in that: Leverage multiple processing threads to concurrently perform power path search and objective function evaluation, including: The power allocation scheme is decomposed into multiple subtasks and the power path search is performed in parallel by multiple processing threads; After each round of search, all path results are uniformly evaluated and pheromones are updated based on the evaluation results.

4. The method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to claim 3, characterized in that: Update pheromones based on the evaluation results, including: The power allocation path is determined to be a good path or a poor path based on the evaluation results; When the power allocation path is a good path, increasing the pheromone of the power allocation path; When the power allocation path is a poor quality path, the pheromone of the power allocation path is reduced.

5. The method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to claim 2, characterized in that: According to the operating status of the flywheel energy storage system and the power variation response model of the thermal power unit, the target power variation value is decomposed using an improved ant colony algorithm to obtain a power allocation plan, which also includes: During the path search process, an adaptive elimination mechanism is set up to automatically exclude paths that do not meet the flywheel energy safety threshold or thermal power ramp limit.

6. The method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to claim 1, characterized in that: The method further comprises: Obtaining an energy state parameter of the flywheel energy storage system, and comparing the energy state parameter with a set threshold; When the energy state parameter is not within the set range, the flywheel energy storage system is not allowed to participate in frequency regulation; When the energy state parameter is within the set range, the flywheel energy storage system is allowed to participate in the frequency regulation task.

7. The method for optimizing frequency regulation of a thermal power unit based on flywheel energy storage according to claim 1, characterized in that: The method further comprises: The instructions of the thermal power units are ramped and the upper limit of the maximum change rate is set.

8. A frequency regulation optimization device for thermal power units based on flywheel energy storage, characterized in that: The device comprises: The calculation module is used to receive the AGC frequency modulation instruction issued by the dispatching center in real time and calculate the difference between the AGC frequency modulation instruction and the target power variation value of the thermal power unit power; a decomposition module, configured to decompose the target power variation value using an improved ant colony algorithm according to the operating state of the flywheel energy storage system and the power variation response model of the thermal power unit to obtain a power allocation plan; The distribution module is used to send power instructions to the thermal power generation unit and the flywheel energy storage system according to the power distribution plan, and the thermal power generation unit and the flywheel energy storage system operate according to the distributed power instructions.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the frequency regulation optimization method for a thermal power unit based on flywheel energy storage according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the frequency regulation optimization method for a thermal power unit based on flywheel energy storage according to any one of claims 1 to 7.

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