An intelligent control method for energy charging and discharging of a flywheel array system

Abstract

The application discloses an intelligent control method for energy charging and discharging of a flywheel array system, and aims at solving the problem of how to realize energy optimal distribution and reduce energy loss of the flywheel array system. The method creatively proposes an optimal solution mode of energy distribution and energy loss, and is related and fused into PID control of each flywheel motor based on the innovation point, so that the rapidity, stability and robustness of the whole system control are improved. Meanwhile, the application fully considers the uncertainty of each flywheel in the array system, and is more practical and applicable.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control, and specifically relates to the intelligent control technology of flywheel array systems. Background Technology

[0002] With increasing attention paid to energy and environmental issues, renewable energy sources such as wind and solar power have been widely adopted. However, these renewable energy sources are intermittent, and large-scale grid integration can cause imbalances in supply and demand, affecting grid stability, reliability, and power quality. Using large-capacity energy storage devices can decouple the generation and demand sides, ensuring a balance between supply and demand. Flywheel energy storage stores energy in the form of mechanical energy within a high-speed rotating flywheel rotor. Due to its high power and energy density, lack of environmental pollution, high conversion efficiency, long service life, wide operating temperature range, and unlimited charge / discharge cycles, it has been widely used in various industries.

[0003] To achieve greater energy storage, higher power, and longer backup time, large-capacity flywheel energy storage units can be developed, or multiple modular flywheel energy storage units can be connected in parallel to form a flywheel array energy storage system. However, a single large-capacity flywheel energy storage unit not only significantly increases costs but may also be limited by technical conditions, such as the possibility of breakage when the flywheel rotor speed is too high. In comparison, flywheel array energy storage systems not only reduce costs but also greatly simplify the research and development process, making them a better solution for achieving large-capacity, high-power energy storage. Research on coordinated control strategies for flywheel array energy storage systems, both domestically and internationally, is relatively limited. For example, array capacity optimization design and coordinated control strategies are rarely addressed. Therefore, there is no complete intelligent coordinated control scheme and algorithm for array systems, meaning that in practical engineering, the problem of simultaneously achieving optimal energy distribution and minimizing energy loss during the charging and discharging of flywheel array systems cannot be solved. Summary of the Invention

[0004] This invention proposes an intelligent control method for charging and discharging energy in a flywheel array system, in order to solve the problem of how to achieve optimal energy distribution and reduce energy loss in the charging and discharging of a flywheel array system.

[0005] A smart control method for charging and discharging energy in a flywheel array system includes the following steps:

[0006] (1) Obtain the relationship curve between the current stored energy value and the current speed value of each flywheel, and write it in the controller as a sub-function for later use;

[0007] (2) The flywheel array system collects data when the flywheels are not working and stores it in the controller. It then fits the function of the energy storage value of each flywheel from full capacity to stop rotating over time, and generates the free energy release function of each flywheel in the non-working state and stores it in the controller for later use.

[0008] (3) The controller detects the current remaining capacity value of n flywheels. When the system is in the charging state, flywheels in the fully charged state are removed; when the system is in the discharging state, flywheels in the stopped state are removed, and the current stored energy value of the remaining m flywheels is stored in the CPU as an array variable.

[0009] (4) Calculate the free energy release function and the measured current stored energy array value to obtain the free energy release time points of m flywheels. Then, infer the energy release state of the flywheel, i.e. the speed of free energy release. Store the energy release state value of each flywheel and its corresponding flywheel energy value in the CPU in the form of array variables.

[0010] (5) Compare the flywheel array system scheduling command value with the total rechargeable and dischargeable energy value of the flywheels. If the system is in a charging state, when the command value is less than the flywheel rechargeable energy value corresponding to the minimum value in the array described in step (3), no energy allocation is made, and this flywheel is used for charging alone; when the command value is greater than the sum of the element values ​​in the array described in step (3), no energy allocation is made, and the system charges all flywheels with the maximum charging power; if the system is in a discharging state, when the command value is less than the flywheel rechargeable energy value corresponding to the maximum value in the array described in step (3), no energy allocation is made, and this flywheel is used for discharging alone; when the command value is greater than the sum of the element values ​​in the array described in step (3), no energy allocation is made, and the system executes all flywheels to release energy to the outside with the maximum discharge power.

[0011] (6) If the instruction value is not in all the states described in step (5), the elements of the arrays described in step 3 and step (4) stored in the CPU are respectively brought into the optimal solution function. The optimal solution function is obtained by recombining the two functions stored in the controller in steps (1) and (2). The recombining function takes into account both the optimal allocation of energy and the reduction of energy loss, and obtains a new array. Thus, each flywheel refers to the elements and the number of elements in the new array to re-discharge energy with different power or different charge and discharge energy values.

[0012] (7) Combine the optimal solution value of charge and discharge energy distribution obtained in step (6) to tune the PID parameters of the corresponding single flywheel online. After the charge or discharge energy reaches Δt time, return to step (3) to realize cyclic measurement control. The Δt is taken as an integer multiple of the incremental PID time step.

[0013] Furthermore, in step (1), the current stored energy value E of the k-th flywheel is...k Relationship with the current speed value w: E k =e k (w), where e k (*) represents the mapping between the two; in step (2), the free energy release function of the kth flywheel in the non-operating state is: W k =f k (t), where W k Let f be the free energy released when the k-th flywheel is not in operation, t be time, and f be the free energy released. k (*) represents the mapping between the two.

[0014] Furthermore, in step (3), the current energy value array of the n flywheels is [E1, E2, E3...E... n The remaining m flywheels, after their current energy values ​​are reordered, form an array [E1, E2, E3...E...]. m ]; In step (4), combined with W n =f n (t) The inverse calculation yields the array of free energy dissipation states of m flywheels.

[0015] Furthermore, in step (5), the system activates the single flywheel energy release condition as follows: p i <max{E1,E2,E3...E n The system controls the activation of all flywheel energy release under the following conditions: The system will enable charging of a single flywheel under the following conditions: in The energy storage value is calculated by subtracting the current energy storage value from the full energy storage value of the nth flywheel; the system activates charging for all flywheels under the following conditions: p i This is the instruction value.

[0016] Furthermore, in step (6), the optimal charging solution function is: Where C is a constant that needs to be freely proportioned according to actual requirements, and F k {*} is a reorganization function; the resulting array is... Therefore, the energy charging weight array for the m flywheels is obtained as follows: The optimal solution formula for energy release is: The permutation results in a new array [E1*f1'(t), E2*f2'(t), E3*f3'(t)...E m *f m From this, we obtain the formula for the energy release weight array of m flywheels: '(t)]. And through the equilibrium formula p = p i / (m-1) for p iA year-on-year reduction compensation is applied, where p is the compensated value.

[0017] Furthermore, in step (7), the formula for the outer loop PID incremental control of the q-th flywheel in the k-th step is:

[0018]

[0019] Where: u q (k) represents the k-th PID output of the q-th flywheel controller; e q (k) represents the error of the kth operation of the qth flywheel controller; kp q The proportional gain of the q-th flywheel controller is used for energy control. During energy release control Ti q The integral time constant of the q-th flywheel controller; Td q The differential time constant of the q-th flywheel controller.

[0020] By adopting the above solution, the present invention has the following beneficial effects:

[0021] 1. Compared with many current array system control methods, this invention incorporates the remaining capacity and disabling status of each flywheel into the energy distribution of the flywheel array system through a clever algorithm. It not only considers the energy distribution problem, but also the energy loss problem, thus responding to the national low-carbon policy.

[0022] 2. This invention is compatible with the addition or reduction of flywheels in later array systems, regardless of their model parameters. Even in the event of a malfunction, due to the intelligence of the control algorithm, the system will automatically adjust the energy distribution method or value to meet the actual needs.

[0023] 3. Based on point 1, the system effectively integrates the global optimal energy allocation result with the corresponding individual flywheel PID control, thereby improving the system's speed, stability, and robustness, and making it more practical for engineering operation and application. Attached Figure Description

[0024] Figure 1 This is a general flowchart of an intelligent control method for a flywheel array system according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of an intelligent control method for the (charging section) of a flywheel array system according to another embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the overall Simulink simulation model of an intelligent control flywheel array system according to an embodiment of the present invention;

[0027] Figure 4This is a Simulink simulation data diagram of an intelligent control system for a flywheel array system according to an embodiment of the present invention. Detailed Implementation

[0028] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 and Figure 2 The flowcharts show the overall process flow of the intelligent control method for the flywheel array system and the flowchart of the intelligent control method for the charging part of the flywheel array system.

[0030] This invention utilizes Simulink simulation software in MATLAB to implement the intelligent control method of this flywheel array system through simulation. By setting different flywheel motor and flywheel parameters, PID parameter settings, and other parameters, it simulates different operating conditions, demonstrating significant advantages over previous control simulations of array systems. Step 1: Build a single motor model, a flywheel model, the motor's SVPWM, and a single-motor dual-closed-loop control structure, connecting them to form a single flywheel energy storage system; Step 2: Copy multiple single flywheel energy storage structures obtained in Step 1, setting the bridge arm of each flywheel energy storage structure, motor parameters (initial speed, output form, viscosity coefficient, friction coefficient, etc.), and inner and outer loop PID parameter settings; Step 3: Build an intelligent control block diagram, connecting the flywheel energy storage system and linking the PID parameter tuning of each flywheel; Step 4: Set different operating conditions and observe the corresponding flywheel speed curves. Figure 3 As shown, four flywheel motor models with different moments of inertia, static friction coefficients, and initial velocities are set up, with flywheel numbers ①②③④ respectively. Figure 4 The figure shows the speed response curves of the four flywheels. The curves clearly demonstrate that this control method is superior to previous control methods.

[0031] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. An intelligent control method for flywheel array system charge and discharge, characterized in that, Includes the following steps: (1) Obtain the relationship curve between the current stored energy value and the current rotation speed value of each flywheel, and write it in the controller as a sub-function for later use; (2) The flywheel array system collects data when the flywheels are not working and stores it in the controller. It then fits the function of the energy storage value of each flywheel from full capacity to stop rotating over time, and generates the free energy release function of each flywheel in the non-working state and stores it in the controller for later use. (3) The controller detects the current remaining capacity value of n flywheels. When the system is in the charging state, flywheels in the fully charged state are removed; when the system is in the discharging state, flywheels in the stopped state are removed, and the current stored energy value of the remaining m flywheels is stored in the CPU as an array variable. (4) Calculate the free energy release function and the measured current stored energy array value to obtain the free energy release time points of m flywheels. Then, infer the energy release state of the flywheel, i.e. the speed of free energy release. Store the energy release state value of each flywheel and its corresponding flywheel energy value in the CPU in the form of array variables. (5) Compare the flywheel array system scheduling command value with the total rechargeable and dischargeable energy value of the flywheels. If the system is in a charging state, when the command value is less than the flywheel rechargeable energy value corresponding to the minimum value in the array variable form described in step (3), no energy allocation is made, and this flywheel is used for charging alone; when the command value is greater than the sum of the element values ​​in the array variable form described in step (3), no energy allocation is made, and the system charges all flywheels with the maximum charging power; if the system is in a discharging state, when the command value is less than the flywheel rechargeable energy value corresponding to the maximum value in the array variable form described in step (3), no energy allocation is made, and this flywheel is used for discharging alone; when the command value is greater than the sum of the element values ​​in the array variable form described in step (3), no energy allocation is made, and the system executes all flywheels to release energy to the outside with the maximum discharge power. (6) If the instruction value is not one of the cases recorded in step (5), the elements of the array variable form in step 3 and the array variable form in step (4) stored in the CPU are respectively brought into the optimal solution function. The optimal solution function is obtained by recombining the two functions stored in the controller in steps (1) and (2). The optimal solution function takes into account both the optimal allocation of energy and the reduction of energy loss, and obtains a new array. Thus, each flywheel refers to the elements and the number of elements in the new array to charge and discharge energy with different power or different charge and discharge energy values. (7) Combine the optimal solution value of charge and discharge energy distribution obtained in step (6) to tune the PID parameters of the corresponding single flywheel online. After the charge or discharge energy reaches Δt time, return to step (3) to realize cyclic measurement control. The Δt is taken as an integer multiple of the incremental PID time step.

2. The intelligent control method for energy charging and discharging of a flywheel array system according to claim 1, characterized in that, In step (1), the current stored energy value of the kth flywheel in relation to the current rotational speed value where is a mapping between the two; in step (2), the free energy release function of the kth flywheel in the non-working state: where is the free energy release of the kth flywheel in the non-working state, t is time, is a mapping between the two.​ 3. The intelligent control method for energy charging and discharging of a flywheel array system according to claim 2, wherein, In step (3), the n flywheel current energy value array is [ , , ... ]; the remaining m flywheel current energy value array reordered is [ , , ... ]; in step (4), the m flywheel pre-free energy state array is obtained by inverse calculation combined with =[ , , ].​ 4. The intelligent control method for energy charging and discharging of a flywheel array system according to claim 3, characterized in that, In step (5), the system enables single flywheel energy release condition <max{ , , ... }, the system control enables all flywheel energy release condition is: ; the system enables single flywheel energy storage condition <max{ , , ... }, wherein The nth flywheel full energy storage value minus the current energy storage value; The system enables all flywheel charging conditions are: ; is the command value.

5. The intelligent control method for energy charging and discharging of a flywheel array system according to claim 4, characterized in that, In step (6), the charging optimal solution function is { , })=C , wherein C is a constant that needs to be freely matched according to actual requirements, s=1, 2,..., m, {*=recombination function, the arrangement obtains a new array for , C , C ], thus obtaining the m flywheel arrangement charging weight array formula for , 1 , ]; the discharging optimal solution formula is = , the arrangement obtains a new array for , * , ], thus obtaining the m flywheel arrangement discharging weight array formula for , , ]; And through the balance formula For The same proportion of small compensation, where For the value of compensation.

6. The intelligent control method for energy charging and discharging of a flywheel array system according to claim 5, wherein, In step (7), the kth step operation outer loop PID incremental control formula of the qth flywheel is: * ( (k)-2 (k-1)+ (k-2)); wherein: is the kth PID output of the qth flywheel controller; is the kth operating deviation of the qth flywheel controller; is the proportional amplification factor of the qth flywheel controller, when charging control = , when discharging control = ; is the integral time constant of the qth flywheel controller; is the differential time constant of the qth flywheel controller.

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