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

Abstract

The present application relates to the technical field of power system frequency modulation, and discloses a thermal power unit frequency modulation optimization method and device based on a flywheel energy storage system, the frequency modulation optimization method comprising: receiving an AGC frequency modulation instruction issued by a dispatch center in real time, and calculating a target power variation value of the AGC frequency modulation instruction and a thermal power unit power; using an improved ant colony algorithm to decompose the target power variation value according to an operating state of the flywheel energy storage system and a thermal power unit power variation response model, to obtain a power distribution scheme; issuing a power instruction to the thermal power unit and the flywheel energy storage system according to the power distribution scheme, and operating the thermal power unit and the flywheel energy storage system according to the distributed power instruction. The improved ant colony algorithm is used to realize collaborative power distribution of the flywheel and the thermal power unit, to realize collaborative frequency modulation control, to use the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, to compensate for thermal power response lag, and to improve the overall system frequency response efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system frequency modulation, in particular to a thermal power unit frequency modulation optimization method and device based on flywheel energy storage. BACKGROUND

[0002] With the rapid growth of new energy proportion, the speed and accuracy of frequency regulation of the power grid are put forward higher requirements. The traditional thermal power unit cannot quickly respond to the AGC (automatic generation control) signal in the case of severe load fluctuation due to its large inertia and slow response speed, and there are problems of frequency modulation lag and unstable control.

[0003] The flywheel energy storage system has the characteristics of high power density and millisecond response speed, and shows significant advantages in the short time scale frequency modulation scene. However, most current schemes only run the flywheel as auxiliary energy storage independently, lack of in-depth research on its collaborative control with the thermal power unit, and the overall frequency modulation performance of the system is still difficult to meet the demand of high penetration rate new energy system. SUMMARY

[0004] Therefore, the present application provides a thermal power unit frequency modulation optimization method and device based on flywheel energy storage to solve the problem that most current schemes only run the flywheel as auxiliary energy storage independently, lack of in-depth research on its collaborative control with the thermal power unit, and the overall frequency modulation performance of the system is difficult to meet the demand of high penetration rate new energy system.

[0005] In the first aspect, the present application provides a thermal power unit frequency modulation optimization method based on flywheel energy storage, which comprises:

[0006] Real-time receiving AGC frequency modulation instruction issued by the dispatching center, and calculating the target power difference value of the AGC frequency modulation instruction and the thermal power unit power;

[0007] According to the operation state of the flywheel energy storage system and the power change response model of the thermal power unit, the improved ant colony algorithm is used to decompose the target power difference value to obtain a power distribution scheme;

[0008] According to the power distribution scheme, the power instruction is issued to the thermal power unit and the flywheel energy storage system, and the thermal power unit and the flywheel energy storage system operate according to the distributed power instruction.

[0009] The thermal power unit frequency modulation optimization method based on flywheel energy storage provided by the present application realizes the collaborative power distribution of flywheel and thermal power unit through the improved ant colony algorithm, realizes the collaborative frequency modulation control, uses the flywheel energy storage system to quickly make up the difference between the thermal power unit and the AGC instruction, makes up the response lag of the thermal power, and improves the frequency response efficiency of the overall system.

[0010] In an alternative embodiment, the target power variation value is decomposed using an improved ant colony algorithm according to the energy state parameter of the flywheel energy storage system and the power variation response model of the thermal power unit, to obtain a power distribution scheme, comprising:

[0011] The search space required for optimization is initialized and the control parameters are processed;

[0012] According to the actual operating characteristics of the flywheel energy storage system and the thermal power unit, a power distribution path space is constructed, and the probability of selecting each power distribution path is calculated according to the pheromone function and the heuristic function;

[0013] The real-time operating state parameters of the flywheel energy storage system and the thermal power unit are obtained, and the pheromone function and the heuristic function are dynamically adjusted according to the operating state parameters;

[0014] When the algorithm falls into a local optimum, a chaotic disturbance mechanism is triggered to nonlinearly disturb the power distribution parameters of a preset number of ant colony individuals, and the power distribution parameters are regenerated;

[0015] The power distribution scheme whose score is lower than the average value for a continuous preset number of iterations is reinitialized;

[0016] Multiple processing threads are used to simultaneously perform power path search and target function evaluation;

[0017] When the maximum number of iterations is reached or the target function value of the optimal solution meets the frequency modulation error tolerance condition, the optimal power distribution scheme in the current iteration round is output.

[0018] By introducing an intelligent optimization algorithm under multiple constraints, the stability and robustness of the power distribution strategy are improved.

[0019] In an alternative embodiment, multiple processing threads are used to simultaneously perform power path search and target function evaluation, comprising:

[0020] The power distribution scheme is decomposed into multiple subtasks, and the power path search is performed in parallel by multiple processing threads;

[0021] After each search round ends, all path results are uniformly evaluated, and the pheromone is updated according to the evaluation results.

[0022] In an alternative embodiment, the pheromone is updated according to the evaluation results, comprising:

[0023] According to the evaluation results, the power distribution path is determined to be a good path or a poor path;

[0024] When the power distribution path is a good path, the pheromone of the power distribution path is increased;

[0025] When the power distribution path is a poor path, pheromone of the power distribution path is reduced.

[0026] In an optional embodiment, the target power variation value is decomposed using the 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 distribution scheme, and the method further comprises:

[0027] In the path search process, an adaptive rejection mechanism is set to automatically exclude paths that do not meet the flywheel energy safety threshold or the thermal power climbing limit.

[0028] In an optional embodiment, the method further comprises:

[0029] An energy state parameter of the flywheel energy storage system is acquired, and the energy state parameter is compared with a set threshold value;

[0030] When the energy state parameter is not in the set range, the flywheel energy storage system is not allowed to participate in frequency modulation adjustment;

[0031] When the energy state parameter is in the set range, the flywheel energy storage system is allowed to participate in frequency modulation.

[0032] In an optional embodiment, the method further comprises:

[0033] The instruction of the thermal power unit is ramped, and a maximum change rate upper limit is set.

[0034] In a second aspect, the application provides a flywheel energy storage-based thermal power unit frequency modulation optimization device, which comprises:

[0035] A calculation module is configured to receive an AGC frequency modulation instruction issued by a dispatching center in real time, and calculate a target power variation value of the AGC frequency modulation instruction and the power of the thermal power unit;

[0036] A decomposition module is 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 distribution scheme;

[0037] A distribution module is configured to issue a power instruction to the thermal power unit and the flywheel energy storage system according to the power distribution scheme, and the thermal power unit and the flywheel energy storage system operate according to the distributed power instruction.

[0038] The flywheel energy storage-based thermal power unit frequency modulation optimization device provided by the application realizes collaborative power distribution of the flywheel and the thermal power unit through the improved ant colony algorithm, realizes collaborative frequency modulation control, uses the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, makes up for the response lag of the thermal power unit, and improves the frequency response efficiency of the overall system.

[0039] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are communicatively connected with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the flywheel energy storage based frequency modulation optimization method for thermal power generating unit according to the first aspect or any one of the corresponding embodiments.

[0040] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for making a computer perform the flywheel energy storage based frequency modulation optimization method for thermal power generating unit according to the first aspect or any one of the corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the specific embodiments or prior art technical solutions of the present application, the drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0042] Figure 1 Fig. 1 is a flowchart of the flywheel energy storage based frequency modulation optimization method for thermal power generating unit according to an embodiment of the present application;

[0043] Figure 2 Fig. 2 is a structural block diagram of the flywheel energy storage based frequency modulation optimization device according to an embodiment of the present application;

[0044] Figure 3 Fig. 3 is a hardware structure diagram of the computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0046] The embodiments of the present application provide a flywheel energy storage based frequency modulation optimization method for thermal power generating unit, which decomposes AGC instructions by introducing improved ant colony algorithm to realize dynamic coordinated response of flywheel energy storage system and thermal power generating unit.

[0047] According to the embodiment of the present application, a flywheel energy storage based frequency modulation optimization method for thermal power generating units 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 herein can be executed in an order different from that shown herein.

[0048] In the present embodiment, a flywheel energy storage based frequency modulation optimization method for thermal power generating units is provided, Figure 1 The flowchart of the flywheel energy storage based frequency modulation optimization method for thermal power generating units according to the embodiment of the present application is shown in Figure 1 The flowchart includes the following steps:

[0049] Step S1, real-time receive AGC frequency modulation instructions issued by the dispatch center, and calculate the target power variation value of the AGC frequency modulation instructions and the power of the thermal power generating unit.

[0050] Specifically, the AGC (Automatic Generation Control) frequency modulation instruction is a real-time power regulation instruction generated by the power grid dispatch center according to the system frequency deviation, load fluctuation and other parameters, which is used to control the output of the generator unit and realize the dynamic balance of the power grid frequency. The power system calculates the target power variation value ΔP of the overall system according to the received AGC instruction, combined with the operating state and frequency modulation characteristics of the thermal power generating unit, as the basis for subsequent power regulation and distribution.

[0051] Step S2, according to the operating state of the flywheel energy storage system and the power change response model of the thermal power generating unit, the improved ant colony algorithm is used to decompose the target power variation value to obtain a power distribution scheme.

[0052] Specifically, step S2 includes the following steps:

[0053] Step S21, initialize the search space and control parameters required for optimization.

[0054] In the embodiment of the present application, in the initialization stage, the thermal power generating unit receives the AGC instruction from the dispatch and starts to prepare for the secondary frequency modulation action, and the algorithm system starts to initialize the search space and control parameters required for optimization, including the ant colony size (indicating the number of power distribution scheme candidates), the pheromone weight (indicating the guidance strength of historical experience), the heuristic factor weight (the guidance ability of the current state such as SOC adequacy and unit response delay), the maximum number of iterations (to ensure that the algorithm is completed within a specified time limit), etc. This stage builds an optimization basic framework that meets the real-time scheduling time limit requirement.

[0055] Step S22, according to the actual operating characteristics of the flywheel energy storage system and the thermal power generating unit, construct the power distribution path space, and calculate the probability of selecting each power distribution path according to the pheromone function and the heuristic function.

[0056] In the embodiment of the application, each ant represents a power distribution scheme, and path selection is affected by state variables such as flywheel SOC, power grid frequency deviation, and unit load margin. The probability of individual selecting a path depends on two types of functions: pheromone function reflects accumulated experience of historical good strategies; and 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 given, and when the thermal power is in the low load interval, the weight of its response capability is increased. In order to prevent the emergence of "infeasible solutions" in the strategy space that do not meet the actual operation restrictions, the application introduces an "adaptive elimination mechanism" to automatically exclude paths that do not meet the flywheel energy safety threshold or the thermal power climbing limit during path search, effectively avoiding the risk of frequency modulation failure or equipment overload.

[0057] Step S23, the real-time operation state parameters of the flywheel energy storage system and the thermal power unit are obtained, and the pheromone function and the heuristic function are dynamically adjusted according to the operation state parameters.

[0058] In the embodiment of the application, in order to adapt to the dynamic changes of the power grid frequency, the fluctuations of the flywheel energy state, and the changes of the thermal power regulation capability, the algorithm continuously obtains system feedback information (such as frequency deviation degree, flywheel power curve response lag, thermal power temperature rise state, etc.) during operation, and dynamically adjusts the pheromone and heuristic functions. This strategy enables the algorithm to quickly re-optimize when the system environment mutates, and is suitable for scheduling scenarios under frequent frequency modulation commands.

[0059] Step S24, when the algorithm falls into a local optimum, a chaotic disturbance mechanism is triggered to nonlinearly disturb the power distribution parameters of a preset part of ant individuals, and the power distribution parameters are regenerated.

[0060] In the embodiment of the application, in some AGC instruction scenarios, the system frequency deviation is small, and the target function changes slightly, so the traditional ant colony search is easy to fall into a local optimum or even stagnate. To solve this problem, the application designs a chaotic disturbance triggering mechanism: in continuous multiple iterations, if the optimal target function improvement value is lower than a set threshold, the system determines that it is currently in a local minimum interval; at this time, the chaotic disturbance mechanism is triggered to disturb the positions of a preset part of ant individuals in a nonlinear sequence disturbance manner (such as Logistic mapping) to expand the search range. The preset part of ant individuals is, for example, only the top 30% of ants, but is not limited thereto. This "global jump disturbance" mechanism effectively avoids the risk of missing the global optimal solution due to early local optimal traps, and is extremely critical in power system frequency modulation, which is a multi-constrained non-convex problem.

[0061] Step S25, the power distribution schemes whose scores in continuous preset number of iterations are lower than the average value are reinitialized with power distribution parameters.

[0062] In the embodiment of the present application, after the chaotic disturbance is executed, some individuals may still be unable to jump out of the poor solution set, especially when the flywheel SOC continues to decrease in the high frequency scheduling stage of the system and the response boundary of the thermal power is compressed. Therefore, the algorithm sets an individual "poor solution elimination mechanism" to reinitialize the ants with poor scores in continuous preset rounds of iterations and give them new strategy combinations. The preset rounds are 15, which is only an example and is not limited to this.

[0063] In step S26, the power path search and the target function evaluation are simultaneously executed by using multiple processing threads.

[0064] In the embodiment of the present application, considering that the frequency modulation response window of the power system is usually controlled within 2 seconds, in order to improve the real-time calculation capability, the algorithm supports a parallel path search mechanism. The power distribution scheme is disassembled into multiple subtasks, and the power path search is executed by multiple processing threads in parallel. The power path search and the target function evaluation are simultaneously executed by multiple processing threads, which significantly shortens the single round iteration time.

[0065] After each round of search ends, all path results are uniformly evaluated, and the pheromone is updated according to the evaluation results: the power distribution path is judged to be a good path or a poor path according to the evaluation results; when the power distribution path is a good path, the pheromone of the power distribution path is increased; when the power distribution path is a poor path, the pheromone of the power distribution path is reduced. The evaluation results include comprehensive indexes such as frequency modulation error, power utilization rate, and flywheel life factor. When the power distribution path is determined to be a good path, the good path obtains positive feedback and increases the probability of subsequent path selection; when the power distribution path is determined to be a poor path, the pheromone of the poor path automatically decays and is gradually eliminated.

[0066] In step S27, when the set maximum number of iterations is reached or the target function value of the optimal solution meets the frequency modulation error tolerance condition, the optimal power distribution scheme in the current iteration round is output.

[0067] In the embodiment of the present application, the system determines whether the power distribution in the current round reaches the termination condition, including whether the adjustment error is within the tolerance range and whether the set maximum number of iterations is reached. If the conditions are not met, the distribution scheme is adjusted again and the loop is executed; if the conditions are met, the current adjustment process is ended and the optimal power distribution strategy in the current iteration round is output. Specifically, it includes: 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 proportion and load distribution coefficient of the two. The result is used as the execution input of the control system to directly drive the actions of the flywheel converter and the thermal power adjustment mechanism.

[0068] The core advantage of the algorithm is that the swarm intelligence optimization (ant colony algorithm), the chaotic global jump mechanism and the dynamic environment perception adjustment strategy are combined, so that the efficiency and convergence precision of the parameter optimization of the control strategy are effectively improved, and the algorithm is especially suitable for complex multi-variable scenes such as power system frequency modulation, energy management system optimization and dispatching strategy search. In addition, by introducing the intelligent optimization algorithm under multiple constraints, the stability and robustness of the power distribution strategy are improved.

[0069] Step S3, according to the power distribution scheme, power instructions are issued to the thermal power unit and the flywheel energy storage system, and the thermal power unit and the flywheel energy storage system operate according to the distributed power instructions.

[0070] Specifically, after the optimal power distribution scheme is calculated by improving the ant colony algorithm, two independent power instructions, i.e., flywheel energy storage system instructions and thermal power unit instructions, are generated. After receiving the instructions, the flywheel energy storage system quickly adjusts the motor speed through the converter to quickly make up for the difference between the thermal power unit and the AGC instruction, realizes millisecond-level frequency modulation control, and makes up for the response lag of the thermal power. After receiving the instructions, the thermal power unit slopes the instructions of the thermal power unit, and sets the maximum change rate upper limit, and adjusts the output by the boiler and the turbine adjusting mechanism according to the slope rate, so as to avoid mechanical wear caused by step instructions and reduce life loss. The algorithm aims to realize the collaborative work of the thermal power unit and the flywheel energy storage system in the automatic generation control (AGC) scene, and takes into account the system stability, response speed and energy sustainability.

[0071] The flywheel energy storage-based thermal power unit frequency modulation optimization method provided by the application realizes the collaborative power distribution of the flywheel and the thermal power unit by improving the ant colony algorithm, realizes the collaborative frequency modulation control, uses the flywheel energy storage system to quickly make up for the difference between the thermal power unit and the AGC instruction, makes up for the response lag of the thermal power, and improves the overall system frequency response efficiency.

[0072] In an optional embodiment, before step S21 is performed, the method further comprises:

[0073] Step S201, acquiring the energy state parameter of the flywheel energy storage system, and comparing the energy state parameter with a set threshold.

[0074] Step S202, when the energy state parameter is not in the set range, the flywheel energy storage system is not allowed to participate in frequency modulation adjustment.

[0075] Step S203, when the energy state parameter is in the set range, the flywheel energy storage system is allowed to participate in the frequency modulation task.

[0076] Specifically, the energy state parameter of the flywheel energy storage system, i.e. the current SOC of the flywheel energy storage system. The SOC value is compared with the upper and lower limits of the set threshold. If the SOC value is lower than the lower limit of the set threshold, it means that the flywheel energy storage system is insufficient, and the flywheel system is not used to participate in the current regulation. If the SOC value is within the set range, the flywheel system is allowed to participate in the frequency regulation task. The set range is [set threshold lower limit, set threshold upper limit].

[0077] In the embodiment, a flywheel energy storage based frequency regulation optimization device for thermal power generating units is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again.As used in the following, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is contemplated.

[0078] The embodiment provides a flywheel energy storage based frequency regulation optimization device for thermal power generating units, as shown in the accompanying drawings, comprising: Figure 2

[0079] A calculation module 21 is configured to receive the AGC frequency regulation instruction issued by the dispatching center in real time, and calculate the target power variation value of the AGC frequency regulation instruction and the thermal power generating unit power.

[0080] A decomposition module 22 is configured to decompose the target power variation value using the improved ant colony algorithm according to the energy state parameter of the flywheel energy storage system and the thermal power generating unit power variation response model, to obtain a power distribution scheme.

[0081] A distribution module 23 is configured to distribute the power instruction 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.

[0082] The further function description of the above-mentioned modules and units is the same as the corresponding embodiments described above, and will not be described again.

[0083] The flywheel energy storage based frequency regulation optimization device for thermal power generating units in the embodiment is presented in the form of functional units, and the units herein refer to ASIC (Application Specific Integrated Circuit, Application Specific Integrated Circuit) circuits, processors and memories executing one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0084] This invention provides a frequency regulation optimization device for thermal power units based on flywheel energy storage. By improving the ant colony algorithm, it realizes the coordinated power distribution between the flywheel and the thermal power unit, and achieves coordinated frequency regulation control. The flywheel energy storage system quickly makes up the difference between the thermal power unit and the AGC command, compensates for the thermal power response lag, and improves the overall system frequency response efficiency.

[0085] This invention also provides a computer device having the above-described features. Figure 2 The device shown is a frequency regulation optimization device for thermal power units based on flywheel energy storage.

[0086] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 3 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 3 Take a processor 10 as an example.

[0087] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0088] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0089] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, etc. The data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some alternative embodiments, the memory 20 can optionally include memory that is remotely located with respect to the processor 10, and these remotely located memories can be connected to the computer device through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communications network, and combinations thereof.

[0090] The memory 20 can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a flash memory, a hard disk, or a solid state disk. The memory 20 can also include a combination of the above-mentioned types of memory.

[0091] The computer device also includes a communication interface 30 for enabling the computer device to communicate with other devices or communication networks.

[0092] The embodiments of the present application also provide a computer readable storage medium. The above-mentioned method according to the embodiments of the present application can be implemented in hardware, firmware, or as computer code recorded on a storage medium, or be stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through a network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that the computer, the processor, the microprocessor controller, or the 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 the computer, the processor, or the hardware, the method shown in the above-mentioned embodiments is implemented.

[0093] Although the embodiments of the present application have been described with reference to the drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A flywheel energy storage-based frequency modulation optimization method for thermal power generating units, characterized in that, The method comprises: Real-time receiving AGC frequency modulation instructions issued by the dispatching center, and calculating a target power variation value of the AGC frequency modulation instructions and the power of the thermal power generating unit; According to the operating state of the flywheel energy storage system and the power variation response model of the thermal power generating unit, the improved ant colony algorithm is used to decompose the target power variation value to obtain a power distribution scheme; According to the power distribution scheme, power instructions are issued to the thermal power generating unit and the flywheel energy storage system, and the thermal power generating unit and the flywheel energy storage system operate according to the distributed power instructions; According to the operating state of the flywheel energy storage system and the power variation response model of the thermal power generating unit, the improved ant colony algorithm is used to decompose the target power variation value to obtain a power distribution scheme, comprising: Initializing the search space and control parameters required for optimization; According to the actual operating characteristics of the flywheel energy storage system and the thermal power generating unit, a power distribution path space is constructed, and the probability of selecting each power distribution path is calculated according to the pheromone function and the heuristic function; Obtaining the real-time operating state parameters of the flywheel energy storage system and the thermal power generating unit, and dynamically adjusting the pheromone function and the heuristic function according to the operating state parameters; When the algorithm falls into local optimization, triggering a chaotic disturbance mechanism to nonlinearly disturb the power distribution parameters of the preset part of the ant colony individuals, and regenerating the power distribution parameters; Reinitializing the power distribution parameters of the power distribution scheme whose continuous preset number of iterations is lower than the average value; Simultaneously executing power path search and target function evaluation by using multiple processing threads; When the maximum number of iterations is reached, or the target function value of the optimal solution meets the frequency modulation error tolerance condition, outputting the optimal power distribution scheme in the current iteration round.

2. The flywheel energy storage based frequency modulation optimization method for thermal power generating units according to claim 1, characterized in that, Simultaneously executing power path search and target function evaluation by using multiple processing threads, comprising: Decomposing the power distribution scheme into multiple subtasks, and executing power path search in parallel through multiple processing threads; After each round of search ends, all path results are uniformly evaluated, and the pheromone is updated according to the evaluation results.

3. The flywheel energy storage based frequency modulation optimization method for thermal power generating units according to claim 2, characterized in that, Updating the pheromone according to the evaluation results, comprising: Judging whether the power distribution path is a good path or a poor path according to the evaluation results; When the power distribution path is a good path, increasing the pheromone of the power distribution path; When the power distribution path is a poor path, reducing the pheromone of the power distribution path.

4. The flywheel energy storage based frequency modulation optimization method for thermal power generating units according to claim 1, characterized in that, According to the operating state of the flywheel energy storage system and the power variation response model of the thermal power generating unit, the improved ant colony algorithm is used to decompose the target power variation value to obtain a power distribution scheme, further comprising: In the path search process, an adaptive rejection mechanism is set to automatically exclude paths that do not meet the flywheel energy safety threshold or the thermal power climbing limit.

5. The flywheel energy storage based frequency modulation optimization method for thermal power generating units according to claim 1, characterized in that, The method further comprises: Obtaining the energy state parameters of the flywheel energy storage system, and comparing the energy state parameters with the set threshold; When the energy state parameters are not in the set range, the flywheel energy storage system is not allowed to participate in frequency modulation adjustment; When the energy state parameters are in the set range, the flywheel energy storage system is allowed to participate in frequency modulation tasks.

6. The flywheel energy storage based frequency modulation optimization method for thermal power generating units according to claim 1, characterized in that, The method further comprises: Sloping the instructions of the thermal power generating unit, and setting the upper limit of the maximum variation rate.

7. A flywheel energy storage based frequency modulation optimization device for thermal power generating units, characterized in that, The device comprises: The computing module is configured to receive an AGC frequency modulation instruction issued by a dispatch center in real time, and calculate a target power variation value of the AGC frequency modulation instruction and the power of the thermal power generating unit; The decomposition module is configured to decompose the target power variation value according to an operating state of the flywheel energy storage system and a power variation response model of the thermal power generating unit, and obtain a power distribution scheme by using an improved ant colony algorithm. The distribution module is configured to distribute power instructions 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 instructions. The decomposition module is configured to decompose the target power variation value according to an operating state of the flywheel energy storage system and a power variation response model of the thermal power generating unit, and obtain a power distribution scheme by using an improved ant colony algorithm. The search space and control parameters required for optimization are initialized; According to the actual operating characteristics of the flywheel energy storage system and the thermal power generating unit, a power distribution path space is constructed, and the probability of selecting each power distribution path is calculated according to a pheromone function and a heuristic function; The real-time operating state parameters of the flywheel energy storage system and the thermal power generating unit are obtained, and the pheromone function and the heuristic function are dynamically adjusted according to the operating state parameters; When the algorithm falls into a local optimum, a chaotic disturbance mechanism is triggered to perform nonlinear disturbance on the power distribution parameters of a preset part of ant colony individuals, and the power distribution parameters are regenerated; The power distribution scheme whose score is lower than the average value for a continuous preset number of iterations is reinitialized; Multiple processing threads are used to simultaneously perform power path search and target function evaluation; When the maximum number of iterations is reached or the target function value of the optimal solution meets the frequency modulation error tolerance condition, the optimal power distribution scheme in the current iteration round is output.

8. A computer device, comprising: The memory and the processor are communicatively connected, and the memory stores computer instructions. The computer readable storage medium stores computer instructions for causing a computer to execute the flywheel energy storage based thermal power generating unit frequency modulation optimization method.

9. A computer-readable storage medium, characterized in that, ​

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