Super capacitor hybrid energy storage coupled with thermal power frequency modulation configuration method and system

Abstract

The application discloses a super capacitor hybrid energy storage coupling thermal power frequency modulation configuration method and system, relates to the technical field of power system frequency modulation, and comprises the following steps: establishing a power system model; determining frequency modulation demand based on the power system model; constructing an optimization objective function, optimizing energy storage configuration; constructing a controller model, and optimizing controller parameters. The application effectively improves the frequency response capability of the power system by reasonably configuring the super capacitor hybrid energy storage system, optimizes the economy, reduces the investment and operation cost, and realizes the best balance between the frequency modulation effect and the economy. In addition, the application has strong adaptability and can be applied to different types and scales of power systems. By adjusting the model parameters and the optimization algorithm, the flexibility and efficiency of the frequency modulation configuration are ensured, thereby bringing remarkable beneficial effects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system frequency modulation, specifically to a super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method and system. BACKGROUND

[0002] With the increasing demand for clean energy in the power system, how to improve the frequency response capability of the system while ensuring economic feasibility has become an important direction of current research. Traditional frequency modulation methods often rely on the regulation ability of thermal power plants, but the regulation response speed of thermal power plants is relatively slow, which is difficult to meet the rapidly changing frequency demand. In recent years, energy storage technology has been increasingly concerned in the power system, especially super capacitor energy storage, which has great potential in power system frequency modulation due to its fast charging and discharging, high power density and long cycle life.

[0003] However, a single energy storage technology is often difficult to meet the complex and variable demand of the power system. Therefore, combining super capacitor with other energy storage technologies (such as lithium battery energy storage, super capacitor) to form a hybrid energy storage system can fully utilize their respective advantages and achieve more efficient frequency modulation effect. However, how to reasonably configure the capacity and parameters of the hybrid energy storage system to achieve the best balance between frequency response capability and economy is a problem to be solved. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: the present application aims to solve the problem of balancing frequency response capability and economy in power system frequency modulation. By reasonably configuring the capacity and parameters of the hybrid energy storage system composed of super capacitor and other energy storage technologies (such as lithium battery), the advantages of fast charging and discharging and high power density of super capacitor, as well as the energy density advantages of other energy storage technologies such as lithium battery, are fully utilized to achieve fast response and economic and efficient operation of power system frequency modulation.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method, comprising the following steps,

[0007] establishing a power system model; determining frequency modulation demand based on the power system model; constructing an optimization objective function to optimize energy storage configuration; constructing a controller model to optimize controller parameters.

[0008] The establishment of the power system model includes constructing a power system framework, and designing a two-region interconnected power system model containing thermal power plants, super capacitor hybrid energy storage systems and loads.

[0009] The super capacitor hybrid energy storage system includes a super capacitor system and a lithium battery system.

[0010] According to the actual power system data, the specific parameters of the thermal power plant, the energy storage system and the load are set, and the initial parameter range of the super capacitor and the lithium battery is set.

[0011] The capacity of the super capacitor system and the lithium battery system is configured, and in the configuration process, the investment and operation and maintenance cost of the super capacitor system and the lithium battery system is minimized while the frequency modulation benefit F is maximized, which is represented as,

[0012] ,

[0013] Among them, is the whole life auxiliary thermal power frequency modulation benefit of the hybrid energy storage, is the total cost of the hybrid energy storage, is represented as,

[0014] ,

[0015] Among them, is the income of frequency modulation service, is the number of days of hybrid energy storage participating in thermal power frequency modulation, and r is the discount rate, is the whole life cycle.

[0016] The total cost of the hybrid energy storage is represented as,

[0017] ,

[0018] Among them, is the investment cost of the energy storage system, is the depreciation cost of the energy storage, is the operation and maintenance cost of the energy storage system.

[0019] As a preferred scheme of the super capacitor hybrid energy storage coupling thermal power frequency modulation configuration method, wherein: the main constraint conditions of the super capacitor energy storage in the super capacitor system include energy state safety constraint and charge and discharge function constraint.

[0020] The energy state safety constraint is represented as,

[0021] ,

[0022] Among them, and are the minimum and maximum capacity of the super capacitor respectively;

[0023] The charge and discharge function constraint is represented as,

[0024] ,

[0025] Among them, The maximum charge-discharge power of the super capacitor.

[0026] As a preferred scheme of the super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method, the constraint condition of the lithium battery system is energy storage capacity constraint, expressed as,

[0027] ,

[0028] wherein, , Emin and Emax represent the minimum and maximum energy storage capacity of the lithium battery, respectively.

[0029] As a preferred scheme of the super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method, the constraint condition of the thermal power plant includes power output constraint and ramp rate constraint.

[0030] The power output constraint is expressed as,

[0031] ,

[0032] wherein, , Emin and Emax represent the minimum and maximum output power of the thermal power plant, respectively.

[0033] The ramp rate constraint is expressed as,

[0034] ,

[0035] wherein, is the maximum ramp rate of the thermal power plant.

[0036] As a preferred scheme of the super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method, the optimization of the energy storage configuration using the NS-GA2 algorithm includes randomly generating an initial solution set of the super capacitor system and the lithium battery system, evaluating each solution according to the predetermined objective function, and calculating the fitness value.

[0037] All solutions in the population are sorted and ranked by non-dominated level, and the dominant solutions in the current population are distinguished and assigned to different non-dominated frontiers, and the dominant solutions are preferentially reserved as optimal candidate solutions.

[0038] Within the same non-dominated level, the crowding degree is calculated.

[0039] Excellent solutions are selected from the combined population of parents and children, and child solutions are generated by combining the characteristics of parent solutions through crossover operation.

[0040] The non-dominated sorting, crowding degree calculation and selection operation are repeatedly performed until the preset maximum iteration number is reached or the convergence condition is met.

[0041] As a preferred scheme of the super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method, wherein: the controller model is constructed, and the controller parameter optimization includes constructing a PI controller model according to the actual situation of the power system, and setting an initial parameter range of the PI controller.

[0042] The objective function of the PI controller parameter optimization is set, and the PSO algorithm is used for optimization to output the optimized proportional coefficient and integral coefficient of the PI controller.

[0043] Another object of the present application is to provide a super capacitor hybrid energy storage coupled thermal power frequency modulation configuration system, which can solve the problems of slow response speed, low regulation accuracy of existing thermal power frequency modulation, and mismatch between energy density and power density of single energy storage system in frequency modulation application through intelligent scheduling and optimization control strategy.

[0044] To solve the above technical problems, the present application provides the following technical scheme: a super capacitor hybrid energy storage coupled thermal power frequency modulation configuration system, comprising a power system model module, an energy storage configuration optimization module, a super capacitor system module, a lithium battery system module and a controller model module.

[0045] The power system model module is responsible for establishing a two-area interconnected power system model of the thermal power plant, the super capacitor hybrid energy storage system and the load.

[0046] The specific parameters of the thermal power plant, the energy storage system and the load are set, and the initial parameter range of the super capacitor and the lithium battery is set.

[0047] The capacity of the super capacitor system and the lithium battery system is configured, and the investment and operation and maintenance cost is optimized.

[0048] The energy storage configuration optimization module is responsible for constructing an optimization objective function for optimization of the energy storage system configuration, and the NS-GA2 is used for optimization to generate an initial solution set and evaluate the fitness of each solution according to the objective function, and finally obtain the optimal solution.

[0049] The super capacitor system module sets the constraint conditions of the super capacitor energy storage, including energy state safety constraint and charge and discharge function constraint.

[0050] The lithium battery system module sets the energy storage capacity constraint of the lithium battery, including minimum and maximum energy storage capacity.

[0051] The controller model module constructs a PI controller model according to the actual situation of the power system, and sets an initial parameter range.

[0052] The parameters of the PI controller, including the proportional coefficient and the integral coefficient, are optimized by the particle swarm optimization algorithm.

[0053] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration method when executing the computer program.

[0054] A computer readable storage medium stores a computer program, and the computer program implements the steps of the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration method when executed by a processor.

[0055] The beneficial effects of the present application are as follows: 1. Improved frequency response capability: By reasonably configuring the capacity and parameters of the supercapacitor hybrid energy storage system, the frequency response capability of the power system can be significantly improved. When frequency deviation occurs, the energy storage system can quickly respond and adjust power, effectively suppressing frequency fluctuations.

[0056] 2. Improved economy: The present application optimizes the energy storage configuration and controller parameters to achieve the best balance between frequency response capability and economy. While ensuring frequency modulation effect, the investment and operating cost of the energy storage system is reduced, improving the overall economy of the system.

[0057] 3. Strong adaptability: The method of the present application can be applied to different types of power systems and frequency modulation requirements. By adjusting the model parameters and optimization algorithm, the optimal frequency modulation configuration of power systems of different scales and structures can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0059] Figure 1 The overall flowchart of the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration method provided for the first embodiment of the present application.

[0060] Figure 2 The overall framework diagram of the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration system provided for the second embodiment of the present application.

[0061] Figure 3 The image after parameter optimization in the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration method provided for the third embodiment of the present application.

[0062] Figure 4 The frequency deviation comparison curve in the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration method provided for the third embodiment of the present application.

[0063] In the figure: 100 power system model module; 200 energy storage configuration optimization module; 300 super capacitor system module, 400 lithium battery system module; 500 controller model module. DETAILED DESCRIPTION

[0064] To make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0065] Embodiment 1, reference Figure 1 For an embodiment of the present application, a super capacitor hybrid energy storage coupled thermal power frequency modulation configuration method is provided, characterized in that:

[0066] S1: Establish a power system model.

[0067] Including, 1) Construct a power system framework: design a two-area interconnected power system model containing thermal power plants, super capacitor hybrid energy storage systems (including super capacitors and lithium batteries) and loads. This model is the basis for subsequent analysis and optimization, ensuring that the model can accurately reflect the dynamic frequency response characteristics of the power system. 2) Determine system parameters: according to actual power system data, set the specific parameters of thermal power plants, energy storage systems and loads, set the initial parameter range of super capacitors and lithium batteries for subsequent optimization.

[0068] Specifically, establishing a power system model includes constructing a power system framework and designing a two-area interconnected power system model containing thermal power plants, super capacitor hybrid energy storage systems and loads.

[0069] Further supplementing the power system model proposed by the present application is a power system cost model, which is used to minimize the investment and operation and maintenance costs of the super capacitor system and the lithium battery system as proposed in the following content.

[0070] The super capacitor hybrid energy storage system includes a super capacitor system and a lithium battery system.

[0071] According to actual power system data, set the specific parameters of thermal power plants, energy storage systems and loads, and set the initial parameter range of super capacitors and lithium batteries.

[0072] Configure the capacity of the super capacitor system and the lithium battery system. During the configuration process, the investment and operation and maintenance costs of the super capacitor system and the lithium battery system are minimized while the frequency modulation benefit F is maximized, which is represented as,

[0073] ,

[0074] wherein, is the benefit of hybrid energy storage full life cycle auxiliary thermal power frequency regulation, is the total cost of hybrid energy storage, is expressed as,

[0075] ,

[0076] wherein, is the benefit of frequency regulation service, is the number of days of thermal power frequency regulation participated by hybrid energy storage, and r is the discount rate, is the full life cycle.

[0077] The total cost of hybrid energy storage is expressed as,

[0078] ,

[0079] wherein, is the investment cost of energy storage system, is the depreciation cost of energy storage, is the operation and maintenance cost of energy storage system.

[0080] The main constraint conditions of supercapacitor energy storage in supercapacitor system include energy state safety constraint and charge-discharge function constraint.

[0081] The energy state safety constraint is expressed as,

[0082] ,

[0083] wherein, and are the minimum and maximum capacity of supercapacitor, respectively;

[0084] The charge-discharge function constraint is expressed as,

[0085] ,

[0086] wherein, is the maximum charge-discharge power of supercapacitor.

[0087] As a preferred scheme of the supercapacitor hybrid energy storage coupling thermal power frequency regulation configuration method, wherein: the constraint condition of lithium battery in the lithium battery system is energy storage capacity constraint, which is expressed as,

[0088] ,

[0089] wherein, , are the minimum and maximum energy storage capacity of lithium battery, respectively.

[0090] As a preferred scheme of the supercapacitor hybrid energy storage coupled thermal power frequency modulation configuration method, wherein: the constraint conditions of the thermal power plant include power output constraint and climbing rate constraint.

[0091] The power output constraint is expressed as,

[0092] ,

[0093] Wherein, 、 Pmin and Pmax are the minimum and maximum output power of the thermal power plant;

[0094] The climbing rate constraint is expressed as,

[0095] ,

[0096] Wherein, is the maximum climbing rate of the thermal power plant.

[0097] S2: Determine the frequency modulation demand based on the power system model.

[0098] Specifically, 1) analyze the load change, collect and analyze the actual load data of the power system based on the power system model constructed in S1, and predict the future load demand. Determine the trend and regularity of load change, which provides the basis for frequency modulation demand, because load change is one of the main reasons for frequency fluctuation, 2) set the frequency modulation parameters: according to the allowed range of frequency deviation (±0.1Hz) and the response speed and duration requirements of frequency modulation, set the threshold of frequency modulation demand. The setting of these parameters will be based on the model constructed in step one and the load change analyzed in step two, to ensure that the power system can respond to frequency fluctuations stably and quickly.

[0099] S3: Construct an optimization objective function to optimize the energy storage configuration.

[0100] 1) Construct the optimization objective function: based on S1 and S2, consider the investment cost, operation and maintenance cost of the energy storage system, and the frequency modulation income, etc., to construct the optimization objective function. The objective function should reflect the influence of the energy storage system capacity, power and charging and discharging strategy on the frequency response capability and economy. 2) Set the constraint condition: according to the actual situation of the power system, set the upper and lower limits of the capacity, power and other parameters of the energy storage system. Consider the charging and discharging rate, charging and discharging depth and other constraint conditions of the energy storage system. 3) Optimize by using NS-GA2 algorithm: input the initial energy storage system parameters, and run the NS-GA2 algorithm for iterative optimization. This optimization process will be based on the model constructed in step one and the parameters set in step two, through the continuous iteration of the algorithm, the energy storage system configuration that meets the constraint condition and makes the objective function optimal will be found. 4) Output results: output the optimized super capacitor power (XXMW), capacity (XXMWh) and lithium battery power (YYMW), capacity (YYMWh).

[0101] Specifically, NS-GA2 (Non-dominated Sorting Genetic Algorithm II) is a multi-objective optimization algorithm used in the present application, which is based on the architecture of genetic algorithm, and through non-dominated sorting and congestion calculation means, the diversity of the solution set is maintained, and it is suitable for solving optimization problems involving multiple conflicting objectives. The core idea of the algorithm is to simulate the natural selection process and gradually approach the Pareto optimal solution set. In the field of multi-objective optimization, it effectively balances the superiority (i.e. non-dominated solution) and diversity (i.e. uniform distribution) of the solution, ensuring that the obtained solution can fully cover the Pareto front of the target space.

[0102] The present application uses NS-GA2 algorithm to optimize the power and capacity of the energy storage system, and the specific operation steps are as follows:

[0103] 1) Population initialization stage: randomly generate the initial solution set of super capacitor and lithium battery system, representing the possible configuration scheme. According to the given objective function, evaluate each solution and calculate its fitness value, which reflects the advantages and disadvantages of each solution in meeting the system performance requirements.

[0104] 2) Non-dominated sorting step: all solutions in the population are sorted by non-dominated level, and the superior solutions in the current population are distinguished. These superior solutions are assigned to different non-dominated frontiers, and are preferentially reserved as optimal candidate solutions.

[0105] 3) Congestion calculation link: within the same non-dominated level, the congestion is calculated to measure the spatial distance between solutions, ensuring the diversity of population solutions in multi-dimensional space and improving the global search ability of the algorithm.

[0106] 4) Selection, Crossover, and Mutation Operations: Select superior solutions from the combined population of parents and offspring, combine parent solution characteristics through crossover operations to generate potentially superior offspring solutions, and introduce random variations through mutation operations to increase genetic diversity and prevent premature convergence to local optima.

[0107] 5) Iterative Evolution Process: Repeat the non-dominated sorting, crowding calculation, and selection operations until the maximum number of iterations is reached or the convergence criteria are met, such as no significant changes in solution diversity or satisfactory solution quality. Through iterative evolution, the optimal or satisfactory solution of the problem is gradually approached.

[0108] S4: Based on the power system model and the optimized energy storage configuration, construct a controller model and optimize the controller parameters.

[0109] 1) Construct the controller model: Based on the power system model constructed in S1 and the optimized energy storage system configuration in S3, construct the model of the PI controller. This model will be used for subsequent optimization of controller parameters. 2) Set the optimization goal: To improve the frequency response performance and control accuracy of the system, set the objective function for PI controller parameter optimization. 3) Optimize using PSO algorithm: Input the initial PI controller parameters and run the PSO algorithm for iterative optimization. This optimization process will be based on the models constructed in S1, S2 and S3, the parameters set and the optimized energy storage system configuration. Through continuous iteration of the algorithm, the optimal PI controller parameter configuration that makes the objective function optimal will be found. 4) Output results: Output the optimized PI controller proportional coefficient ( =a) and integral coefficient ( =b).

[0110] Further supplement, by constructing the controller model to optimize the parameters of the PI controller, the controller can respond more quickly to frequency fluctuations in the power system, reducing the duration of frequency deviation and improving the dynamic stability of the system.

[0111] The proportional coefficient ( ) and integral coefficient ( ) of the PI controller directly affect the response speed and stability of the power system. The optimized parameters can ensure that the system quickly adjusts the charge and discharge power of the energy storage system when there is a load mutation or power generation fluctuation, maintaining the frequency within the allowed range.

[0112] The optimized PI controller can more accurately regulate the output power of the energy storage system, reduce the amplitude of frequency deviation, and ensure that the system frequency always remains within the ±0.1Hz allowed range proposed in S2.

[0113] By optimizing the parameters of the PI controller through the PSO algorithm, the optimal and The combination enables the power system to maintain high control accuracy in both steady state and dynamic processes, and avoids excessive fluctuation of frequency.

[0114] Embodiment 2, refer to Figure 2 For an embodiment of the present application, a system for configuring a super capacitor hybrid energy storage coupled thermal power frequency modulation is provided, characterized in that: comprising a power system model module 100, an energy storage configuration optimization module 200, a super capacitor system module 300, a lithium battery system module 400, and a controller model module 500.

[0115] The power system model module 100 is responsible for establishing a two-region interconnected power system model of the thermal power plant, the super capacitor hybrid energy storage system and the load.

[0116] The specific parameters of the thermal power plant, the energy storage system and the load are set, and the initial parameter range of the super capacitor and the lithium battery is set.

[0117] The capacity of the super capacitor system and the lithium battery system is configured, and the investment and operation and maintenance cost is optimized.

[0118] The energy storage configuration optimization module 200 is responsible for constructing an optimization objective function for the optimization of the energy storage system configuration, and uses NS-GA2 for optimization to generate an initial solution set and evaluate the fitness of each solution according to the objective function, and finally obtains the optimal solution.

[0119] The super capacitor system module 300 sets the constraint conditions of the super capacitor energy storage, including energy state safety constraints and charge and discharge function constraints.

[0120] The lithium battery system module 400 sets the energy storage capacity constraints of the lithium battery, including the minimum and maximum energy storage capacity.

[0121] The controller model module 500 constructs a PI controller model according to the actual situation of the power system, and sets the initial parameter range.

[0122] The parameters of the PI controller are optimized by the particle swarm optimization algorithm, including the proportional coefficient and the integral coefficient.

[0123] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions of the present application can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instructions execution systems, apparatuses, or devices. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatuses, or devices.

[0125] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting, or otherwise processing, if necessary, in other suitable ways, to be electronically obtained, and then stored in the computer memory.

[0126] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the embodiments described above, the various steps or methods can be implemented, in part, or in whole, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art, can be used to implement the application: a hybrid of the techniques mentioned above; a combination of one or more of the techniques mentioned above; a combination of one or more of the techniques mentioned above with one or more other techniques not mentioned above; and / or one or more other techniques not mentioned above.

[0127] Embodiment 3, with reference to Figures 3-4 In this embodiment, in order to verify the beneficial effects of the application, scientific demonstration is carried out through economic benefit calculation and simulation experiment. The existing traditional method and the method of this embodiment are experimented respectively.

[0128] The dimension of the particle swarm algorithm is set to 2, the PI parameter range is set to [-100, 100], the speed range is [-1, 1], and the particle swarm population size is 100. After 100 times of parameter optimization, the image is as shown in Figure 3 .

[0129] Thus, the parameters are , .

[0130] Figure 4 The frequency deviation contrast curve under step disturbance.

[0131] As can be seen from Figure 4 , by optimizing the PI controller parameters in the frequency modulation system through the particle swarm optimization (PSO) algorithm, the frequency response performance of the system can be significantly improved, which is specifically manifested in that the system frequency deviation is reduced, the recovery time is shortened, and the stability is improved.

[0132] The parameters of different energy storage devices before and after optimization are shown in the following table.

[0133] Table 1 Comparison of parameters of supercapacitors and lithium batteries before and after optimization

[0134] ,

[0135] As can be seen from the table, by optimizing the parameter configuration of the supercapacitor and the lithium battery in cooperation, the power and capacity requirements of the two are reduced. That is, according to the optimization frequency modulation strategy proposed in the application, the power and capacity requirements of the equipment can be reduced without reducing the frequency modulation performance, the investment and operation cost of the system is reduced, the purchase and maintenance cost of the energy storage equipment is reduced, and thus the economic efficiency of the system is improved.

[0136] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for configuring supercapacitor-coupled thermal power frequency regulation with hybrid energy storage, characterized in that, include: Establish a power system model; Frequency regulation requirements are determined based on power system models; Construct an optimization objective function and optimize energy storage configuration; A controller model is constructed based on the power system model and the optimized energy storage configuration, and the controller parameters are optimized. The establishment of the power system model includes constructing a power system framework and designing a two-region interconnected power system model that includes thermal power plants, supercapacitor hybrid energy storage systems, and loads. The supercapacitor hybrid energy storage system includes a supercapacitor system and a lithium battery system; Based on actual power system data, specific parameters for thermal power plants, energy storage systems, and loads are set, and initial parameter ranges for supercapacitors and lithium batteries are set. The configuration of the supercapacitor system and lithium battery system, aiming to minimize the investment and operation / maintenance costs during the configuration process, is expressed as follows: ， in, To enhance the frequency regulation benefits of hybrid energy storage throughout its entire lifecycle, The total cost of hybrid energy storage, Represented as, ， in, For the revenue of FM service, The number of days that hybrid energy storage participates in frequency regulation of thermal power plants, where r is the discount rate. For the entire life cycle; The total cost of the hybrid energy storage is expressed as follows: ， in, The investment cost of the energy storage system, For energy storage depreciation costs, This refers to the operation and maintenance costs of the energy storage system.

2. The method for configuring supercapacitor hybrid energy storage coupled with thermal power frequency regulation as described in claim 1, characterized in that: The main constraints on supercapacitor energy storage in the supercapacitor system include energy state security constraints and charging / discharging function constraints. The energy state security constraint is expressed as follows: ， in, and These are the minimum and maximum capacities of supercapacitors, respectively. The charging and discharging function constraint is expressed as follows: ， in, This represents the maximum charging and discharging power of the supercapacitor.

3. The method for configuring supercapacitor hybrid energy storage coupled with thermal power frequency regulation as described in claim 2, characterized in that: The constraint on the lithium battery in the lithium battery system is the energy storage capacity constraint, expressed as follows: ， in, , These represent the minimum and maximum energy storage capacities of a lithium battery, respectively.

4. The method for configuring supercapacitor hybrid energy storage coupled with thermal power frequency regulation as described in claim 3, characterized in that: The constraints of the thermal power plant include power output constraints and ramp rate constraints. The power output constraint is expressed as follows: ， in, , These are the minimum and maximum output power of the thermal power plant; The climbing rate constraint is expressed as follows: ， in, This represents the maximum ramp rate of the thermal power plant.

5. The supercapacitor hybrid energy storage coupled thermal power frequency regulation configuration method as described in claim 4, characterized in that: The optimization of the energy storage configuration using the NS-GA2 algorithm includes: randomly generating initial solution sets for the supercapacitor system and the lithium battery system; evaluating each solution according to a predetermined objective function; and calculating the fitness value. All solutions in the population are classified and sorted according to non-dominated hierarchy, and the dominant solutions in the current population are distinguished. The dominant solutions are assigned to different non-dominated frontiers and are preferentially retained as the optimal candidate solutions. Calculate crowding within the same non-dominated hierarchy; Select excellent solutions from the combined population of parent and offspring, and generate offspring solutions by combining the features of parent solutions through crossover operations; Repeatedly perform non-dominated sorting, crowding calculation, and selection operations until the preset maximum number of iterations is reached or the convergence condition is met.

6. The method for configuring supercapacitor hybrid energy storage coupled with thermal power frequency regulation as described in claim 5, characterized in that: The construction of the controller model and optimization of the controller parameters include, based on the actual situation of the power system, constructing a PI controller model and setting the initial parameter range of the PI controller; Set the objective function for PI controller parameter optimization, use the PSO algorithm for optimization, and output the optimized PI controller proportional coefficient and integral coefficient.

7. A system employing the supercapacitor hybrid energy storage coupled thermal power frequency regulation configuration method as described in any one of claims 1 to 6, characterized in that: It includes a power system model module (100), an energy storage configuration optimization module (200), a supercapacitor system module (300), a lithium battery system module (400), and a controller model module (500). The power system model module (100) is responsible for establishing a two-region interconnected power system model of thermal power plants, supercapacitor hybrid energy storage systems, and loads; Set specific parameters for thermal power plants, energy storage systems, and loads, as well as the initial parameter ranges for supercapacitors and lithium batteries; Configure the capacity of supercapacitor and lithium battery systems to optimize investment and operation and maintenance costs; The energy storage configuration optimization module (200) is responsible for constructing the optimization objective function for optimizing the energy storage system configuration. It uses NS-GA2 for optimization, generates an initial solution set, evaluates the fitness of each solution according to the objective function, and finally obtains the optimal solution. The supercapacitor system module (300) sets constraints on supercapacitor energy storage, including energy state security constraints and charge / discharge function constraints. The lithium battery system module (400) sets energy storage capacity constraints for the lithium battery, including minimum and maximum energy storage capacity; The controller model module (500) constructs a PI controller model and sets the initial parameter range according to the actual situation of the power system. The parameters of the PI controller, including the proportional and integral coefficients, are optimized using the particle swarm optimization algorithm.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the supercapacitor hybrid energy storage coupled thermal power frequency regulation configuration method according to any one of claims 1 to 6.

9. 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 steps of the supercapacitor hybrid energy storage coupled thermal power frequency regulation configuration method according to any one of claims 1 to 6.

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