Particle swarm optimization-based flywheel-lithium battery hybrid energy storage system participation secondary frequency modulation capacity configuration method

Abstract

The invention discloses a particle swarm optimization-based flywheel-lithium battery hybrid energy storage system participation secondary frequency modulation capacity configuration method, which comprises the steps of establishing a regional power grid frequency modulation dynamic model based on ACE distribution and ARR distribution, and comprehensively considering an ACE signal distribution mode and an ARR signal distribution mode to determine a frequency modulation power instruction distributed by a hybrid energy storage system; a first-order low-pass filter is used for carrying out high and low frequency division on a frequency modulation power signal distributed by the hybrid energy storage system, a high-frequency component is distributed to flywheel energy storage for response, a low-frequency component is distributed to a lithium battery for response, and the high-frequency component and the low-frequency component serve as reference power of flywheel energy storage and lithium battery energy storage; the charging and discharging power of hybrid energy storage is adjusted and controlled by considering different energy storage SOC reconstruction domains; a hybrid energy storage capacity configuration model with the maximum hybrid energy storage participation frequency modulation income as the target is established based on the whole life cycle, and a particle swarm optimization algorithm is adopted for solving; the overall economical efficiency and safety of the hybrid energy storage system participating in frequency modulation are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid energy storage system participating in power grid frequency regulation, and in particular to a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency regulation based on particle swarm optimization. Background Art

[0002] With the continuous progress of science and technology and the rapid development of the economy, the scale of the power system is also constantly developing and expanding. The total installed capacity continues to increase, and the voltage level continues to increase, which puts more stringent requirements on the stability of the system frequency. The role of Automatic Generation Control (AGC) in regional power grids is becoming more and more significant. The electrochemical energy storage system has the characteristics of fast ramp rate and short response time. Compared with frequency regulation by thermal power units alone, the use of electrochemical energy storage to assist thermal power units in frequency regulation can more effectively improve the frequency regulation performance of regional power grids.

[0003] When energy storage batteries participate in grid frequency regulation, there are two distribution modes: ARR signal distribution and ACE signal distribution. In the early stage of frequency regulation, the distribution mode based on ACE signals can effectively reduce frequency deviation; in the middle and late stages of frequency regulation, the distribution mode based on ARR signals is conducive to reducing frequency recovery time. Finding the appropriate switching time between ARR and ACE signals can avoid fluctuations caused by large differences in amplitude before and after the switching of frequency modulation signal modes, thereby reducing the maximum frequency deviation in the early stage of frequency regulation and shortening the frequency recovery time in the middle and late stages of frequency regulation.

[0004] Appropriate power command signal decomposition methods are the premise for ensuring the economic stability of the system. Commonly used power signal decomposition methods include wavelet transform, empirical mode decomposition (EMD), etc. However, EMD is prone to modal aliasing, the decomposition effect is poor, and it is impossible to accurately separate signals with similar frequencies; the effect of wavelet transform mainly depends on the selection of fundamental value, and its accuracy depends on the fluctuation of the original signal. Using a suitable command signal decomposition method can improve the economy of the system. Considering the SOC reconstruction domain to control the charging and discharging of energy storage under different SOC states can improve the frequency modulation effect of hybrid energy storage. In summary, the above-mentioned power allocation methods generally have certain problems and limitations in application, and the current cost-benefit analysis of energy storage SOC control strategy and energy storage participation in frequency modulation is not in-depth enough. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a capacity configuration method for a flywheel-lithium battery hybrid energy storage system participating in secondary frequency regulation based on particle swarm optimization, so as to effectively improve the overall economy and safety of the hybrid energy storage system participating in frequency regulation.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization, which comprises the following steps:

[0007] Step 1) establish a regional power grid frequency modulation dynamic model based on ACE allocation and ARR allocation, comprehensively consider the ACE signal allocation mode and the ARR signal allocation mode to determine the frequency modulation power command allocated by the hybrid energy storage system, and use the intersection of the time domain response curves of the ACE and ARR signals as the time point for switching the frequency modulation signal allocation mode;

[0008] Step 2), using a first-order low-pass filter to divide the frequency-modulated power signal allocated by the hybrid energy storage system into high and low frequencies, the high-frequency component is allocated to the flywheel energy storage for response, and the low-frequency component is allocated to the lithium battery for response, and is used as the reference power for the flywheel energy storage and the lithium battery energy storage;

[0009] Step 3), adjusting and controlling the charging and discharging power of the hybrid energy storage by considering the different energy storage SOC reconstruction domains;

[0010] Step 4) Based on the whole life cycle, a hybrid energy storage capacity configuration model is established with the goal of maximizing the benefits of hybrid energy storage participating in frequency regulation, and the particle swarm optimization algorithm is used to solve it.

[0011] Preferably, the specific process of step 1) is as follows:

[0012] Establish a regional power grid frequency regulation dynamic model based on ACE frequency regulation power signal distribution:

[0013]

[0014] Where: ΔP L (s), (Ms+D), ΔF(s) are load disturbance, machine-grid interface model and frequency deviation, respectively, where M, D are grid inertia time constant and load damping coefficient, respectively, G g (s) is the transfer function model of the thermal power unit, G Ni (s) is the transfer function model of the energy storage battery, K G , K I , B are the power coefficient, integral controller coefficient and frequency deviation coefficient of the primary frequency regulation unit, respectively. and They are the primary and secondary frequency modulation output of thermal power units and the secondary frequency modulation output of energy storage batteries, P gs (s) and P Ni (s) are the secondary frequency regulation responsibilities of thermal power units and energy storage batteries, respectively, and ACE(s) is the frequency regulation signal allocated by the energy storage system;

[0015] Among them G Ni (s) and Gg (s) can be expressed as:

[0016]

[0017] Where: T PCS is the time constant of the energy storage battery power conversion system, F HP 、T RH is the gain and time constant of the reheater of the thermal power unit, T G 、T CH is the time constant of the thermal power unit speed governor and steam turbine;

[0018] Combining the above formula, it can be seen that the frequency modulation signal allocated by the hybrid energy storage system is:

[0019]

[0020] Establish a regional power grid frequency regulation dynamic model based on ARR frequency regulation power signal distribution:

[0021]

[0022] From the above formula, it can be seen that the frequency modulation signal allocated to the hybrid energy storage system to receive ARR is:

[0023]

[0024] The above formula can be used to obtain the time domain response curves of the ACE signal and the ARR signal of the hybrid energy storage response. The intersection of the time domain response curves of the ACE and ARR signals is used as the time point for switching the frequency modulation signal allocation mode. When the frequency modulation process starts, the mode based on the ACE signal allocation is first adopted, and then the second frequency modulation output of each frequency modulation power supply is determined according to the above power command allocation strategy. Then, the output of the frequency modulation power supply is substituted into the following formula as the frequency modulation signal switch. If the timing criterion changes from greater than zero to less than zero, the signal allocation mode will also be converted from ACE to ARR:

[0025] R t =ACE(s)-ARR(s).

[0026] Preferably, in step 2), a first-order low-pass filter is used to filter the frequency-modulated power signal P allocated to the hybrid energy storage system. ACE / ARR (s) The process of dividing high and low frequencies is as follows:

[0027]

[0028] Where: P ACE / ARR (s) is the frequency modulation power signal allocated to the hybrid energy storage system, s is a complex variable in the complex plane, P l (s) and P h(s) are respectively the low-frequency signal and the high-frequency signal of the frequency-modulated power signal allocated by the hybrid energy storage system represented in the complex frequency domain after being decomposed by a low-pass filter; T f is the filter time constant;

[0029] Using d / dt to represent the differential operator in the above formula, after differentiation, we get:

[0030]

[0031] Where: ΔT is the increment value of the counter each time in the loop, P ACE / ARR (t-1), P l (t-1) and P h (t-1) are the power of the low-frequency signal and high-frequency signal at the next moment after the ACE / ARR signal and the frequency modulation power signal allocated by the hybrid energy storage system are decomposed by the low-pass filter;

[0032] The high-frequency component is assigned to the flywheel energy storage for response, and the low-frequency component is assigned to the lithium battery for response:

[0033]

[0034] Where: P L (t) is the frequency modulation power signal of the flywheel energy storage response, P F (t) is the frequency modulation power signal of the flywheel energy storage response, P h (t) and P l (t) are the high-frequency power and low-frequency power of the hybrid energy storage demand ACE / ARR command signal after being decomposed by the filter at time t.

[0035] Preferably, the specific process of step 3) is as follows:

[0036] Based on the different SOC states of flywheel energy storage and lithium battery energy storage, the charge and discharge power of the flywheel and lithium battery is controlled, and the SOC state is partitioned and reconstructed into 9 areas. The 9 areas from bottom to top and from left to right in the reconstructed domain graph are named R1, R2, R3, R4, R5, R6, R7, R8, and R9 respectively; the horizontal and vertical axes in the figure are the charge state values ​​of lithium battery energy storage and flywheel energy storage, respectively, and SOC is used to represent the charge state of the flywheel energy storage. l and SOC h They are the low warning value and high warning value of energy storage SOC respectively. When the energy storage SOC value is between 0-SOC l The energy storage is in the charging priority area. When the energy storage SOC value is in SOC l -SOC h When the energy storage SOC value is in the SOC range, the energy storage is in the normal working area. h In the -1 range, the energy storage is in the discharge priority area;

[0037] According to different reconstruction domains, corresponding charging and discharging control strategies are proposed, which ultimately achieves efficient coordinated optimization of flywheel and lithium battery energy storage, avoids overcharging and overdischarging of the two energy storage components, and improves the safety and stability of system operation.

[0038] In the reconstruction domains R1, R4, R5, R7, R8, and R9, the SOC level of the lithium battery energy storage in the reconstruction domains R4, R7, and R8 is higher than the SOC level of the flywheel energy storage, and the SOC level of the lithium battery energy storage in the reconstruction domains R1, R5, and R9 is the same as the SOC level of the flywheel energy storage. Since the flywheel energy storage can be charged and discharged arbitrarily without a limit on the number of times, the lithium battery energy storage must reserve a certain capacity as a frequency regulation standby in the next frequency regulation stage, so the charging and discharging strategy of lithium battery energy storage first discharging and flywheel energy storage first charging is adopted, as shown in the following formula:

[0039]

[0040] Where: and are the discharge power of lithium battery energy storage and flywheel energy storage at time t, and are the charging power of lithium battery energy storage and flywheel energy storage at time t, and are the maximum charge and discharge power of lithium battery energy storage and flywheel energy storage at time t, P h (t) and P l (t) are the high-frequency power and low-frequency power of the ACE / ARR command signal of the hybrid energy storage demand at time t after being decomposed by the filter, and P(t) is the frequency modulation command of the hybrid energy storage at time t;

[0041] In the reconstruction domains R2, R3, and R6, the SOC level of the flywheel energy storage is higher than that of the lithium battery energy storage. Since the storage capacity of the lithium battery energy storage is large, the flywheel energy storage must reserve a certain capacity as a standby for the next stage of frequency regulation. Therefore, the charge-discharge strategy of flywheel energy storage is adopted, which is to discharge first and charge first, as shown in the following formula:

[0042]

[0043] Preferably, the specific process of step 4) is as follows:

[0044] Establish a profit model for hybrid energy storage participating in frequency regulation:

[0045]

[0046] Where: J p is the comprehensive index of frequency modulation performance, D jis the jth AGC regulation depth, which is the contribution of the unit to the grid frequency stability in the jth AGC frequency regulation. When the specified power is reached before receiving the next AGC instruction, D i =|P A -P Si |, when the specified power is not reached D i =|P Ei -P Si |, Y AGC Compensation unit price for AGC frequency modulation performance;

[0047] Establishing the initial investment cost model of hybrid energy storage system:

[0048]

[0049] Where: and are the unit power costs of lithium battery energy storage and flywheel energy storage, and are the rated powers of lithium battery energy storage and flywheel energy storage, and are the unit capacity costs of lithium battery energy storage and flywheel energy storage, and are the rated capacities of lithium battery energy storage and flywheel energy storage, r is the discount rate, n is the number of replacements, n = T LCC / T life , T life is the service life of the lithium battery, T LCC For the full life cycle of energy storage;

[0050] Establish a hybrid energy storage system cost recovery model:

[0051]

[0052] Where: and are the unit power recovery cost and unit capacity recovery cost of lithium battery energy storage, respectively, and j is the jth recovery;

[0053] Daily revenue of hybrid energy storage participating in grid frequency regulation C total The objective function is as follows:

[0054] max(C total )=max(C J -C1-C2);

[0055] The model constraints are specifically:

[0056] Energy storage charging and discharging power constraints:

[0057]

[0058] Constraints of state of charge on energy storage:

[0059]

[0060] Among them: SOC L (t) and SOC F (t) are the charge states of the lithium battery and flywheel energy storage at time t, and They are the low warning value and high warning value of the lithium battery charge state, and are the low warning value and high warning value of the flywheel energy storage charge state, η is the charge and discharge efficiency of energy storage, η L and η F are the charging and discharging efficiency of lithium battery and flywheel energy storage, P S (t) is the power at time t, Δt is the sampling interval, E is the energy storage capacity, and are the minimum charging power and maximum charging power of lithium battery energy storage at time t, and are the minimum discharge power and maximum discharge power of lithium battery energy storage at time t, and are the minimum charging power and maximum charging power of the flywheel energy storage at time t, and are the minimum discharge power and maximum discharge power of the flywheel energy storage at time t respectively;

[0061] Power balance constraints:

[0062] The power required for frequency regulation of hybrid energy storage is equal to the output power of lithium battery and flywheel energy storage at time t;

[0063] P(t)=P L (t)+P F (t);

[0064] The maximum charge and discharge power and cumulative capacity of the flywheel and lithium battery are taken as the minimum reference values ​​of the rated power and rated capacity, respectively, and the particle swarm optimization algorithm is used to solve the capacity configuration model.

[0065] Preferably, the steps of using particle swarm optimization algorithm to solve the capacity configuration model are as follows:

[0066] (1) Set the initial population size of the particle swarm algorithm to 50, the maximum number of iterations to 100, the acceleration factors C1 and C2 to 1.5, and the maximum and minimum inertia factors to 1 and 0.5 respectively;

[0067] (2) Initialize the population, randomly generate particles that meet the constraints, and set the initial position and velocity of each particle; calculate the fitness value, record the individual optimal position and optimal value of the particle, and the global optimal position and optimal value of the particle swarm;

[0068] (3) Start iterative calculation, update the speed and position of the particle, and process the boundary conditions of the particle. If the particle crosses the boundary, it will be processed to obtain a new generation of particle population; compare the fitness of each particle in the new generation of population with the current optimal solution, and update the individual and global optimal solutions;

[0069] (4) When the maximum number of iterations is met, the search process ends; the global optimal solution is output and the algorithm ends.

[0070] Beneficial effects of the present invention:

[0071] 1. The present invention comprehensively considers the ACE signal allocation mode and the ARR signal allocation mode to determine the frequency modulation power instruction allocated by the hybrid energy storage system, and uses the intersection of the time domain response curves of the ACE and ARR signals as the time point for switching the frequency modulation signal allocation mode, which can avoid the fluctuation caused by the large difference in the amplitude before and after the switching of the frequency modulation signal mode, reduce the maximum frequency deviation in the early stage of frequency modulation, and shorten the frequency recovery time in the middle and late stages of frequency modulation;

[0072] 2. The present invention uses a first-order low-pass filter to divide the frequency-modulated power signal allocated by the hybrid energy storage system into high and low frequencies, and can accurately separate the required frequency signal;

[0073] 3. The present invention adjusts and controls the charge and discharge power of the hybrid energy storage by considering the different energy storage SOC reconstruction domains, and can adjust the SOC states of the two energy storages, thereby improving the life and frequency modulation effect of the hybrid energy storage;

[0074] 4. The present invention adopts an improved optimization algorithm to solve the capacity optimization configuration model, which can obtain the optimal configuration more quickly and accurately;

[0075] 5. The present invention establishes a hybrid energy storage capacity configuration model with the goal of maximizing the benefits of hybrid energy storage participating in frequency regulation, which can quantitatively analyze the capacity configuration results and economy of hybrid energy storage participating in frequency regulation. The improved optimization algorithm is used to solve the capacity optimization configuration model, which can more quickly and accurately obtain the optimal configuration. Overall, the use of this step for capacity optimization configuration reduces the configuration power and capacity, and improves the overall economy of the system.

[0076] 6. The present invention effectively improves the economy and safety of the hybrid energy storage system participating in the overall frequency regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1A flowchart of a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization provided by the present invention;

[0078] Figure 2 The figure is a dynamic model diagram of frequency regulation of regional power grid based on ACE allocation of hybrid energy storage;

[0079] Figure 3 The dynamic model diagram of regional power grid frequency regulation based on ARR allocation of hybrid energy storage;

[0080] Figure 4 It is the time domain response curve diagram of ACE and ARR signals;

[0081] Figure 5 Reconstruct the domain diagram for the hybrid energy storage unit SOC;

[0082] Figure 6 System frequency deviation curves in ACE signal, ARR signal and comprehensive signal distribution modes. DETAILED DESCRIPTION

[0083] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0084] like Figure 1 As shown, a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency regulation based on particle swarm optimization includes the following steps:

[0085] Step 1) Establish a regional power grid frequency regulation dynamic model based on ACE allocation and ARR allocation, comprehensively consider the ACE signal allocation mode and the ARR signal allocation mode to determine the frequency regulation power command allocated by the hybrid energy storage system, and use the intersection of the time domain response curves of the ACE and ARR signals as the time point for switching the frequency regulation signal allocation method.

[0086] Establish a regional power grid frequency regulation dynamic model based on ACE frequency regulation power signal distribution, such as Figure 2 As shown in the figure, ΔP L (s), (Ms+D), ΔF(s) are load disturbance, machine-grid interface model and frequency deviation, respectively, where M, D are grid inertia time constant and load damping coefficient, respectively, G g (s) is the transfer function model of the thermal power unit, G Ni (s) is the transfer function model of the energy storage battery, K G , K I , B are the power coefficient, integral controller coefficient and frequency deviation coefficient of the primary frequency regulation unit, respectively. and They are the primary and secondary frequency modulation output of thermal power units and the secondary frequency modulation output of energy storage batteries, P gs (s) and PNi (s) are the secondary frequency regulation responsibilities of thermal power units and energy storage batteries respectively, and ACE(s) is the frequency regulation signal allocated by the energy storage system.

[0087] As can be seen from the figure:

[0088]

[0089] Among them G Ni (s) and G g (s) can be expressed as:

[0090]

[0091] Where: T PCS is the time constant of the energy storage battery power conversion system, F HP 、T RH is the gain and time constant of the reheater of the thermal power unit, T G 、T CH It is the time constant of the speed governor and steam turbine of the thermal power unit.

[0092] Combining the above formula, it can be seen that the frequency modulation signal allocated by the hybrid energy storage system is:

[0093]

[0094] Establish a regional power grid frequency regulation dynamic model based on ARR frequency regulation power signal distribution, such as Figure 3 As shown:

[0095]

[0096] From the above formula, it can be seen that the frequency modulation signal allocated to the hybrid energy storage system to receive ARR is:

[0097]

[0098] The time domain response curves of the ACE signal and ARR signal of the hybrid energy storage response can be obtained from the above formula, such as Figure 4 As shown, the intersection of the time domain response curves of the ACE and ARR signals is used as the time point for switching the frequency modulation signal distribution method. When the frequency modulation process starts, the mode based on ACE signal distribution is first adopted, and then the second frequency modulation output of each frequency modulation power supply is determined according to the above power command distribution strategy. Then, the output of the frequency modulation power supply is substituted into the following formula as the frequency modulation signal switch. If the timing criterion changes from greater than zero to less than zero, the signal distribution mode will also be converted from ACE to ARR.

[0099] R t =ACE(s)-ARR(s)

[0100] Step 2) using a first-order low-pass filter to divide the frequency-modulated power signal allocated by the hybrid energy storage system into high and low frequencies, the high-frequency component is allocated to the flywheel energy storage for response, and the low-frequency component is allocated to the lithium battery for response, and serves as the reference power for the flywheel energy storage and the lithium battery energy storage;

[0101] The frequency modulated power signal P allocated to the hybrid energy storage system is filtered using a first-order low-pass filter. ACE / ARR (s) Divide high and low frequencies:

[0102]

[0103] Where: P l (s) and P h (s) are the low-frequency signal and high-frequency signal of the frequency-modulated power signal allocated by the hybrid energy storage system after being decomposed by a low-pass filter; T f is the filter time constant

[0104] Using d / dt to represent the differential operator in the above formula, after differentiation, we get:

[0105]

[0106] Where: ΔT is the increment value of the counter each time in the loop, P ACE / ARR (t-1), P l (t-1) and P h (t-1) are the powers of the low-frequency signal and the high-frequency signal at the next moment after the ACE / ARR signal and the frequency-modulated power signal allocated by the hybrid energy storage system are decomposed by the low-pass filter.

[0107] The high-frequency component is assigned to the flywheel energy storage for response, and the low-frequency component is assigned to the lithium battery for response:

[0108]

[0109] Where: P L (t) is the frequency modulation power signal of the flywheel energy storage response, P F (t) is the frequency modulated power signal that the flywheel energy storage responds to.

[0110] Step 3) adjusting and controlling the charging and discharging power of the hybrid energy storage by considering the different energy storage SOC reconstruction domains;

[0111] Based on the different SOC states of flywheel energy storage and lithium battery energy storage, the charge and discharge power of the flywheel and lithium battery are controlled, and the SOC state is partitioned and reconstructed into 9 areas, such as Figure 5 As shown in the figure, the horizontal and vertical axes are the state of charge values ​​of lithium battery energy storage and flywheel energy storage, respectively, with SOC l and SOC hThey are the low warning value and high warning value of energy storage SOC respectively. When the energy storage SOC value is between 0-SOC l The energy storage is in the charging priority area. When the energy storage SOC value is in SOC l -SOC h When the energy storage SOC value is in the SOC range, the energy storage is in the normal working area. h In the -1 range, the energy storage is in the discharge priority area.

[0112] For different reconstruction domains, corresponding charging and discharging control strategies are proposed, ultimately achieving efficient coordinated and optimized operation of flywheel and lithium battery energy storage, avoiding overcharging and over-discharging of the two energy storage elements, and improving the safety and stability of system operation.

[0113] In the reconstruction domains R1, R4, R5, R7, R8, and R9, the SOC level of the lithium battery energy storage in the reconstruction domains R4, R7, and R8 is higher than the SOC level of the flywheel energy storage, and the SOC level of the lithium battery energy storage in the reconstruction domains R1, R5, and R9 is the same as the SOC level of the flywheel energy storage. Since the flywheel energy storage can be charged and discharged arbitrarily without a limit on the number of times, the lithium battery energy storage must reserve a certain capacity as a frequency regulation standby in the next frequency regulation stage, so the charging and discharging strategy of lithium battery energy storage first discharging and flywheel energy storage first charging is adopted, as shown in the following formula:

[0114]

[0115] Where: and are the discharge power of lithium battery energy storage and flywheel energy storage at time t, and are the charging power of lithium battery energy storage and flywheel energy storage at time t, and are the maximum charge and discharge power of lithium battery energy storage and flywheel energy storage at time t, P h (t) and P l (t) are the high-frequency power and low-frequency power of the hybrid energy storage demand ACE / ARR command signal after being decomposed by the filter at time t, and P(t) is the hybrid energy storage frequency modulation command at time t.

[0116] In the reconstruction domains R2, R3, and R6, the SOC level of the flywheel energy storage is higher than that of the lithium battery energy storage. Since the storage capacity of the lithium battery energy storage is large, the flywheel energy storage must reserve a certain capacity as a standby for the next stage of frequency regulation. Therefore, the charge-discharge strategy of flywheel energy storage is adopted, which is to discharge first and charge first, as shown in the following formula:

[0117]

[0118] Step 4) Based on the whole life cycle, a hybrid energy storage capacity configuration model is established with the goal of maximizing the benefits of hybrid energy storage participating in frequency regulation, and a particle swarm optimization algorithm is used to solve it;

[0119] Establish a profit model for hybrid energy storage participating in frequency regulation:

[0120]

[0121] Where: J p is the comprehensive index of frequency modulation performance, D j is the jth AGC regulation depth, which is the contribution of the unit to the grid frequency stability in the jth AGC frequency regulation. When the specified power is reached before receiving the next AGC instruction, D i =|P A -P Si |, when the specified power is not reached D i =|P Ei -P Si |, Y AGC Compensation unit price for AGC frequency modulation performance.

[0122] Establishing the initial investment cost model of hybrid energy storage system:

[0123]

[0124] Where: and are the unit power costs of lithium battery energy storage and flywheel energy storage, and are the rated powers of lithium battery energy storage and flywheel energy storage, and are the unit capacity costs of lithium battery energy storage and flywheel energy storage, and are the rated capacities of lithium battery energy storage and flywheel energy storage, r is the discount rate, n is the number of replacements, n = T LCC / T life , T life Is the service life of lithium battery.

[0125] Establishing a cost recovery model for hybrid energy storage systems

[0126]

[0127] Daily revenue of hybrid energy storage participating in grid frequency regulation C total The objective function is as follows:

[0128] max(C total )=max(C J -C1-C2)

[0129] The model constraints are specifically:

[0130] Energy storage charging and discharging power constraints:

[0131]

[0132] Constraints of state of charge on energy storage:

[0133]

[0134] Among them: SOC L (t) and SOC F (t) are the charge states of the lithium battery and flywheel energy storage at time t, and They are the low warning value and high warning value of the lithium battery charge state, and are the low warning value and high warning value of the flywheel energy storage charge state, η is the charge and discharge efficiency of energy storage, η L and η F are the charging and discharging efficiency of lithium battery and flywheel energy storage, P S (t) is the power at time t, Δt is the sampling interval, and E is the energy storage capacity.

[0135] Power balance constraints:

[0136] The power demand of hybrid energy storage frequency regulation is equal to the output power of lithium battery and flywheel energy storage at time t.

[0137] P(t)=P L (t)+P F (t)

[0138] The maximum charge and discharge power and cumulative capacity of the flywheel and lithium battery are taken as the minimum reference values ​​of the rated power and rated capacity, respectively. The particle swarm optimization algorithm is used to solve the capacity configuration model. The specific solution steps are as follows:

[0139] (1) Set the initial population size of the particle swarm algorithm to 50, the maximum number of iterations to 100, the acceleration factors C1 and C2 to 1.5, and the maximum and minimum inertia factors to 1 and 0.5 respectively.

[0140] (2) Initialize the population, randomly generate particles that meet the constraints, and set the initial position and velocity of each particle. Calculate the fitness value, record the individual optimal position and optimal value of the particle, and the global optimal position and optimal value of the particle swarm.

[0141] (3) Start iterative calculation, update the speed and position of the particle, and process the boundary conditions of the particle. If the particle crosses the boundary, process it to obtain a new generation of particle population. Compare the fitness of each particle in the new generation population with the current optimal solution, and update the individual and global optimal solutions.

[0142] (4) When the maximum number of iterations is met, the search process ends. The global optimal solution is output and the algorithm ends.

[0143] Example:

[0144] On the MATLAB / Simulink platform, Figure 2 and Figure 3 The system frequency response model is shown in Figure 1. A step load disturbance with a unit value of 0.5 is added to the system, and the simulation duration is set to 100s. The frequency modulation signal distribution mode is compared with the signal distribution mode based on ACE and the signal distribution mode based on ARR. The simulation results are shown in Figure 1. Figure 6 The frequency modulation evaluation indicators are shown in Table 1. Figure 6 It can be clearly seen from Table 1 that the integrated signal distribution mode adopted by the present invention has excellent performance in reducing the maximum frequency deviation in the early stage of frequency modulation and quickly recovering the frequency deviation in the middle and late stages of frequency modulation. Since the ACE signal distribution in the early stage of frequency modulation reduces the transient frequency deviation of the system, compared with the ARR signal distribution mode, the maximum frequency deviation of the system in the integrated signal distribution mode is reduced by 59.1%, and the root mean square value of the frequency deviation is reduced by 25.5%. Compared with the ACE-based signal distribution mode, the root mean square value of the system frequency deviation in the integrated signal distribution mode is reduced by 39.4%. This is because switching to the ARR-based signal distribution mode in the middle and late stages of frequency modulation shortens the system frequency recovery time.

[0145] Table 1 System frequency modulation evaluation indicators under ACE signal, ARR signal and comprehensive signal distribution mode

[0146]

[0147] The present invention limits the flywheel energy storage conversion efficiency to 0.9 and the lithium battery conversion efficiency to 0.8. By using the particle swarm optimization algorithm to calculate the capacity configuration model, the capacity configuration and economic results of the lithium battery and flywheel energy storage can be obtained, as shown in Table 2.

[0148] Table 2 Hybrid energy storage capacity configuration results

[0149]

[0150] If a hybrid energy storage system is not used, the utilization rate of new energy is low and the system benefit is not high because the new energy power generation power exceeds the planned needs of the power plant too much. The capacity configuration through the steps of the present invention reduces the configured power and capacity, and improves the overall economy of the system.

[0151] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization, characterized in that: It includes the following steps: Step 1) establish a regional power grid frequency modulation dynamic model based on ACE allocation and ARR allocation, comprehensively consider the ACE signal allocation mode and the ARR signal allocation mode to determine the frequency modulation power command allocated by the hybrid energy storage system, and use the intersection of the time domain response curves of the ACE and ARR signals as the time point for switching the frequency modulation signal allocation mode; Step 2), using a first-order low-pass filter to divide the frequency-modulated power signal allocated by the hybrid energy storage system into high and low frequencies, the high-frequency component is allocated to the flywheel energy storage for response, and the low-frequency component is allocated to the lithium battery for response, and is used as the reference power for the flywheel energy storage and the lithium battery energy storage; Step 3), adjusting and controlling the charging and discharging power of the hybrid energy storage by considering the different energy storage SOC reconstruction domains; Step 4) Based on the whole life cycle, a hybrid energy storage capacity configuration model is established with the goal of maximizing the benefits of hybrid energy storage participating in frequency regulation, and the particle swarm optimization algorithm is used to solve it.

2. According to claim 1, a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization is characterized in that: Step 1) The specific process is as follows: Establish a regional power grid frequency regulation dynamic model based on ACE frequency regulation power signal distribution: Where: ΔP L (s), (Ms+D), ΔF(s) are load disturbance, machine-grid interface model and frequency deviation, respectively, where M, D are grid inertia time constant and load damping coefficient, respectively, G g (s) is the transfer function model of the thermal power unit, G Ni (s) is the transfer function model of the energy storage battery, K G , K I , B are the power coefficient, integral controller coefficient and frequency deviation coefficient of the primary frequency regulation unit, respectively. and They are the primary and secondary frequency modulation output of thermal power units and the secondary frequency modulation output of energy storage batteries, P gs (s) and P Ni (s) are the secondary frequency regulation responsibilities of thermal power units and energy storage batteries, respectively, and ACE(s) is the frequency regulation signal allocated by the energy storage system; Among them G Ni (s) and G g (s) can be expressed as: Where: T PCS is the time constant of the energy storage battery power conversion system, F HP , T RH is the gain and time constant of the reheater of the thermal power unit, T G , T CH is the time constant of the thermal power unit speed governor and steam turbine; Combining the above formula, it can be seen that the frequency modulation signal allocated by the hybrid energy storage system is: Establish a regional power grid frequency regulation dynamic model based on ARR frequency regulation power signal distribution: From the above formula, it can be seen that the frequency modulation signal allocated to the hybrid energy storage system to receive ARR is: The above formula can be used to obtain the time domain response curves of the ACE signal and the ARR signal of the hybrid energy storage response. The intersection of the time domain response curves of the ACE and ARR signals is used as the time point for switching the frequency modulation signal allocation mode. When the frequency modulation process starts, the mode based on the ACE signal allocation is first adopted, and then the second frequency modulation output of each frequency modulation power supply is determined according to the above power command allocation strategy. Then, the output of the frequency modulation power supply is substituted into the following formula as the frequency modulation signal switch. If the timing criterion changes from greater than zero to less than zero, the signal allocation mode will also be converted from ACE to ARR: R t =ACE(s)-ARR(s)。 3. According to claim 1, a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization is characterized in that: Step 2): Use a first-order low-pass filter to filter the frequency-modulated power signal P allocated to the hybrid energy storage system. ACE / ARR (s) The process of dividing high and low frequencies is as follows: Where: P ACE / ARR (s) is the frequency modulation power signal allocated to the hybrid energy storage system, s is a complex variable in the complex plane, P l (s) and P h (s) are respectively the low-frequency signal and the high-frequency signal of the frequency-modulated power signal allocated by the hybrid energy storage system represented in the complex frequency domain after being decomposed by a low-pass filter; T f is the filter time constant; Using d / dt to represent the differential operator in the above formula, after differentiation, we get: Where: ΔT is the increment value of the counter each time in the loop, P ACE / ARR (t-1), P l (t-1) and P h (t-1) are the power of the low-frequency signal and high-frequency signal at the next moment after the ACE / ARR signal and the frequency modulation power signal allocated by the hybrid energy storage system are decomposed by the low-pass filter; The high-frequency component is assigned to the flywheel energy storage for response, and the low-frequency component is assigned to the lithium battery for response: Where: P L (t) is the frequency modulation power signal of the flywheel energy storage response, P F (t) is the frequency modulation power signal of the flywheel energy storage response, P h (t) and P l (t) are the high-frequency power and low-frequency power of the hybrid energy storage demand ACE / ARR command signal after being decomposed by the filter at time t.

4. According to claim 1, a flywheel-lithium battery hybrid energy storage system based on particle swarm optimization participating in secondary frequency modulation capacity configuration method is characterized by: Step 3) The specific process is as follows: Based on the different SOC states of flywheel energy storage and lithium battery energy storage, the charge and discharge power of the flywheel and lithium battery is controlled, and the SOC state is partitioned and reconstructed into 9 areas. The 9 areas from bottom to top and from left to right in the reconstructed domain graph are named R1, R2, R3, R4, R5, R6, R7, R8, and R9 respectively; the horizontal and vertical axes in the figure are the charge state values ​​of lithium battery energy storage and flywheel energy storage, respectively, and SOC is used to represent the charge state of the flywheel energy storage. l and SOC h They are the low warning value and high warning value of energy storage SOC respectively. When the energy storage SOC value is between 0-SOC l The energy storage is in the charging priority area. When the energy storage SOC value is in SOC l -SOC h When the energy storage SOC value is in the SOC range, the energy storage is in the normal working area. h In the -1 range, the energy storage is in the discharge priority area; According to different reconstruction domains, corresponding charging and discharging control strategies are proposed, which ultimately achieves efficient coordinated optimization of flywheel and lithium battery energy storage, avoids overcharging and overdischarging of the two energy storage components, and improves the safety and stability of system operation. In the reconstruction domains R1, R4, R5, R7, R8, and R9, the SOC level of the lithium battery energy storage in the reconstruction domains R4, R7, and R8 is higher than the SOC level of the flywheel energy storage, and the SOC level of the lithium battery energy storage in the reconstruction domains R1, R5, and R9 is the same as the SOC level of the flywheel energy storage. Since the flywheel energy storage can be charged and discharged arbitrarily without a limit on the number of times, the lithium battery energy storage must reserve a certain capacity as a frequency regulation standby in the next frequency regulation stage, so the charging and discharging strategy of lithium battery energy storage first discharging and flywheel energy storage first charging is adopted, as shown in the following formula: Where: and are the discharge power of lithium battery energy storage and flywheel energy storage at time t, and are the charging power of lithium battery energy storage and flywheel energy storage at time t, and are the maximum charge and discharge power of lithium battery energy storage and flywheel energy storage at time t, P h (t) and P l (t) are the high-frequency power and low-frequency power of the ACE / ARR command signal of the hybrid energy storage demand at time t after being decomposed by the filter, and P(t) is the frequency modulation command of the hybrid energy storage at time t; In the reconstruction domains R2, R3, and R6, the SOC level of the flywheel energy storage is higher than that of the lithium battery energy storage. Since the storage capacity of the lithium battery energy storage is large, the flywheel energy storage must reserve a certain capacity as a standby for the next stage of frequency regulation. Therefore, the charge-discharge strategy of flywheel energy storage is adopted, which is to discharge first and charge first, as shown in the following formula:

5. According to claim 1, a method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization is characterized in that: Step 4) The specific process is as follows: Establish a profit model for hybrid energy storage participating in frequency regulation: Where: J p is the comprehensive index of frequency modulation performance, D j is the jth AGC regulation depth, which is the contribution of the unit to the grid frequency stability in the jth AGC frequency regulation. When the specified power is reached before receiving the next AGC instruction, D i =|P A -P Si |, when the specified power is not reached D i =|P Ei -P Si |, Y AGC Compensation unit price for AGC frequency modulation performance; Establishing the initial investment cost model of hybrid energy storage system: Where: and are the unit power costs of lithium battery energy storage and flywheel energy storage, and are the rated powers of lithium battery energy storage and flywheel energy storage, and are the unit capacity costs of lithium battery energy storage and flywheel energy storage, and are the rated capacities of lithium battery energy storage and flywheel energy storage, r is the discount rate, n is the number of replacements, n = T LCC / T life , T life is the service life of the lithium battery, T LCC For the full life cycle of energy storage; Establish a hybrid energy storage system cost recovery model: Where: and are the unit power recovery cost and unit capacity recovery cost of lithium battery energy storage, respectively, and j is the jth recovery; Daily revenue of hybrid energy storage participating in grid frequency regulation C total The objective function is as follows: max(C total )=max(C J -C1-C2); The model constraints are specifically: Energy storage charging and discharging power constraints: Constraints of state of charge on energy storage: Among them: SOC L (t) and SOC F (t) are the charge states of the lithium battery and flywheel energy storage at time t, and They are the low warning value and high warning value of the lithium battery charge state, and are the low warning value and high warning value of the flywheel energy storage charge state, η is the charge and discharge efficiency of energy storage, η L and η F are the charging and discharging efficiency of lithium battery and flywheel energy storage, P S (t) is the power at time t, Δt is the sampling interval, E is the energy storage capacity, and are the minimum charging power and maximum charging power of lithium battery energy storage at time t, and are the minimum discharge power and maximum discharge power of lithium battery energy storage at time t, and are the minimum charging power and maximum charging power of the flywheel energy storage at time t, and are the minimum discharge power and maximum discharge power of the flywheel energy storage at time t respectively; Power balance constraints: The power required for frequency regulation of hybrid energy storage is equal to the output power of lithium battery and flywheel energy storage at time t; P(t)=P L (t)+P F (t); The maximum charge and discharge power and cumulative capacity of the flywheel and lithium battery are taken as the minimum reference values ​​of the rated power and rated capacity, respectively, and the particle swarm optimization algorithm is used to solve the capacity configuration model.

6. A method for configuring the capacity of a flywheel-lithium battery hybrid energy storage system participating in secondary frequency modulation based on particle swarm optimization according to claim 5, characterized in that: The steps of using particle swarm optimization algorithm to solve the capacity configuration model are as follows: (1) Set the initial population size of the particle swarm algorithm to 50, the maximum number of iterations to 100, the acceleration factors C1 and C2 to 1.5, and the maximum and minimum inertia factors to 1 and 0.5 respectively; (2) Initialize the population, randomly generate particles that meet the constraints, and set the initial position and velocity of each particle; calculate the fitness value, record the individual optimal position and optimal value of the particle, and the global optimal position and optimal value of the particle swarm; (3) Start iterative calculation, update the speed and position of the particle, and process the boundary conditions of the particle. If the particle crosses the boundary, it will be processed to obtain a new generation of particle population; compare the fitness of each particle in the new generation of population with the current optimal solution, and update the individual and global optimal solutions; (4) When the maximum number of iterations is met, the search process ends; the global optimal solution is output and the algorithm ends.

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