A method for synthesizing gravity energy storage multi-motor grid connection based on greedy algorithm

Abstract

The present invention discloses a method for synthesizing gravity energy storage multi-motor grid connection based on a greedy algorithm, which includes: step S1: initialization, setting the total power demand and the current power output value of each motor; step S2: according to the total power demand and the current power output value of each motor, calculating the influence of increasing or decreasing the power of each motor on the total power demand; step S3: selecting a motor with the smallest influence on the total power demand after the power change; step S4: adjusting the power output of the selected motor and updating the total power demand; step S5: repeating steps S2 to S4 until the total power demand is met or all motors are considered. This scheme can realize simple and effective control logic and reduce the control complexity of the energy storage system through the greedy algorithm. Due to the use of advanced control strategies, this method can significantly improve the operating efficiency of the energy storage system and enhance the stability of the entire energy storage system through the optimization algorithm.

Description

Technical Field

[0001] The invention belongs to the technical field of gravity energy storage motor control, and specifically relates to a method for synthesizing gravity energy storage multi-motor grid connection based on a greedy algorithm. Background Art

[0002] Gravity energy storage system is a system that uses the descent or lifting of heavy objects to store or release energy. Taking the power generation process as an example, a single motor in the gravity energy storage system only outputs basically stable power when the heavy object is descending at a uniform speed. Therefore, it is necessary to distribute and control the power of multiple motors to make the total output power of the system meet the grid connection requirements. The smooth synthesis of power is one of the key factors to ensure the stable operation of the system. Power fluctuations not only affect the efficiency and stability of the system, but also have adverse effects on the power grid. Therefore, it is of great practical significance to achieve the smooth power synthesis of multiple motors. Multi-motor cooperative control technology is a technology that allows multiple motors to work together. By accurately controlling the output power and speed of each motor, the overall system can be optimized. Multi-motor cooperative control technology can improve the stability and efficiency of the system and reduce energy waste. Existing multi-motor power distribution methods are often based on preset rules or complex mathematical models, and adopt strategies such as fuzzy control. However, these methods have certain limitations and challenges in dealing with complex nonlinear systems and changing working environments. For example, fuzzy control is difficult to deal with uncertainty and nonlinear problems. For occasions with high requirements for real-time and flexibility, these methods may not meet the needs. Therefore, a new multi-motor power distribution method is needed to realize the grid connection of multi-motor synthetic gravity energy storage, which can not only meet the real-time requirements but also achieve smooth power distribution. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for grid-connecting multiple motors with synthetic gravity energy storage based on a greedy algorithm, so as to solve the problem that the existing scheme is difficult to meet the occasions with high real-time performance and flexibility, and realize power smoothing of multiple motors with synthetic gravity energy storage through a greedy algorithm, thereby ensuring system stability and improving system operation efficiency.

[0004] According to the technical solution of the present invention, the present invention provides a method for synthesizing gravity energy storage multi-motor grid connection based on a greedy algorithm, which comprises the following steps:

[0005] Step S1: Initialization, setting the total power demand and the current power output value of each motor;

[0006] Step S2: Calculate the impact of increasing or decreasing the power of each motor on the total power demand based on the total power demand and the current power output value of each motor;

[0007] Step S3: Select a motor whose power change has the smallest impact on the total power demand;

[0008] Step S4: adjusting the power output according to the selected motor, reducing the fluctuation of the total power output curve by increasing or decreasing the output power of the motor, and updating the total power demand;

[0009] Step S5: Repeat steps S2 to S4 until the total power demand is met or all motors have been considered.

[0010] Furthermore, step S2 includes:

[0011] Assume that the function f(x1,x2,...,xn) represents the total power demand, where xi is the power output of the i-th motor;

[0012] The gradient of a function represents the rate of change of the function value with respect to each variable, and is represented by a vector:

[0013]

[0014] Iterate the search in the opposite direction of the gradient to find a point where the function value decreases. Each iteration is performed according to the following formula:

[0015]

[0016] Among them, α is the learning rate, which determines the size of each step; through a continuous iterative process, we can eventually find a motor that has the least impact on the total power demand.

[0017] Furthermore, step S3 includes assuming a priority queue PQ, which contains the power changes of all motors and the gradients affecting the total power demand, and each motor i has a priority p(i), where p(i) represents the power change and the gradient, and then the motor selection based on the priority queue is implemented by the following steps:

[0018] Step S31, calculate the power changes and gradients of all motors, and insert them into the priority queue PQ in descending order of priority;

[0019] Step S32, initializing a variable best_p for recording the current minimum priority value; initializing a variable selected_motor for recording the selected motor;

[0020] Step S33, repeat the following steps a to c until the priority queue PQ is empty:

[0021] Step a, delete the element with the highest priority from PQ, that is, motor i with the largest p(i);

[0022] Step b, if p(i) is less than best_p, update best_p and selected_motor to p(i) and i respectively;

[0023] Step c, insert element i at the end of PQ;

[0024] Step S34, returning selected_motor, which is the motor with the smallest impact on the total power demand.

[0025] Furthermore, in step S4, a PID controller is used to adjust the power output of the motor:

[0026] The PID controller consists of three parts: proportional, integral and differential. The proportional part is used to adjust the proportional relationship between the output and input of the system; the integral part is used to eliminate the error of the system; the differential part is used to improve the response speed of the system.

[0027] Assume a motor m with a power output of P m (t), the target power is P t (t), the calculation formula of the PID controller can be expressed as:

[0028] ΔP m (t) = K p (P t (t)-P m (t))+K i ∫(P t (t)-P m (t))dt+K d (P t (t)-P m (t)) / dt,

[0029] Where ΔP m (t) is the power change of motor m, K p , K i and K d They are the proportional coefficient, integral coefficient and differential coefficient of the PID controller respectively;

[0030] By adjusting the parameter K of the PID controller p , K i and K d , which can realize fast and accurate adjustment of motor power output, thereby achieving smooth power distribution.

[0031] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0032] The method for synthesizing gravity energy storage multi-motor grid-connected based on the greedy algorithm of the present invention is designed from the perspective of power engineering application to design a method for smooth power synthesis of multiple motors in a gravity energy storage system, which is helpful to promote the development and popularization of energy storage technology. By optimizing the operating state of the motor to effectively reduce unnecessary energy waste, the smooth power synthesis of multiple motors can ensure the power output stability of the system under various working conditions and improve the overall efficiency of the energy storage system. The coordinated work of multiple motors can enhance the stability of the entire energy storage system, so that it can still maintain a high operating efficiency in the face of various emergencies. Through the greedy algorithm, simple and effective control logic can be implemented to reduce the control complexity of the energy storage system. Due to the use of advanced control strategies, this method can significantly improve the operating efficiency of the energy storage system, thereby reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention provides a method for synthesizing the grid-connected power of multiple motors for gravity energy storage based on a greedy algorithm. In other words, it is a method for smoothly synthesizing the power of multiple motors in a gravity energy storage system based on a greedy algorithm. The method solves the problem that existing solutions are difficult to meet the needs of occasions with high real-time performance and flexibility. The power smoothing of multiple motors for gravity energy storage is achieved through the greedy algorithm, which ensures the stability of the system while improving the system operation efficiency.

[0035] In general, the method of the present invention adopts the best or optimal (i.e., most favorable) choice under the current state in each step of the selection through a greedy algorithm, thereby hoping to result in the best or optimal algorithm. This algorithm does not guarantee that the global optimal solution can be found, but it can guarantee that the local optimal solution can be found, thereby solving some specific problems. In the synthetic gravity energy storage multi-motor power smoothing method, the greedy algorithm can be applied to the power distribution of the motor. The basic idea is to balance the power output of each motor as much as possible while meeting the total power demand.

[0036] Specifically, a method for synthesizing gravity energy storage multi-motor grid connection based on a greedy algorithm in one embodiment of the present invention includes the following steps:

[0037] Step S1: Initialization, setting the total power demand and the current power output value of each motor;

[0038] Step S2: Calculate the impact of increasing or decreasing the power of each motor on the total power demand based on the total power demand and the current power output value of each motor;

[0039] Step S3: Select a motor whose power change has the smallest impact on the total power demand;

[0040] Step S4: adjusting the power output of the selected motor with the smallest impact on the total power, reducing the fluctuation of the total power output curve by increasing or decreasing the output power of the motor, and updating the total power demand;

[0041] Step S5: Repeat steps S2 to S4 until the total power demand is met or all motors have been considered; the total power demand is met, that is, the current total power generation curve fluctuation of the gravity energy storage system is lower than the demand power fluctuation, meeting the grid connection requirements; all motors have been considered, that is, all motors have been adjusted in step S4.

[0042] The specific implementation methods can be adjusted and optimized according to actual needs. Some more specific implementation methods are given below.

[0043] In step S2, the gradient descent method can be used to calculate the effect of increasing or decreasing the power of each motor on the total power demand. The gradient descent method is an optimization algorithm that can quickly find values ​​near the optimal solution. By calculating the gradient of the power change of each motor to the total power demand, the optimal power allocation solution can be found.

[0044] Specifically, each motor can be considered as a variable, and the power output of the motor can be considered as the value of the variable. By calculating the gradient of the power output to the total power demand, the optimal power allocation solution can be found.

[0045] Assume that there is a function f(x1,x2,...,xn) that represents the total power demand, where xi is the power output of the i-th motor. The gradient of the function represents the rate of change of the function value with respect to each variable, which can be represented by a vector:

[0046]

[0047] In the gradient descent algorithm, it is necessary to perform an iterative search in the opposite direction of the gradient to find a point where the function value decreases. Specifically, each iteration can be performed according to the following formula:

[0048]

[0049] Among them, α is the learning rate, which determines the size of each step. Through this iterative process, we can eventually find a motor that has the least impact on the total power demand.

[0050] In step S3, a priority queue can be used to select the motor with the least impact on the total power demand. A priority queue is a data structure that treats each motor as an element. Each element has a priority and can be selected based on the priority of the element. When selecting an element, the element with the highest priority is always selected first. In the case of multi-motor power distribution, the power change of each motor and the gradient that affects the total power demand are used as the priority of the element and inserted into the priority queue. Then, in the order of the priority queue, the motor with the highest priority is selected for adjustment to achieve smooth power distribution. Specifically including:

[0051] Assume a priority queue PQ that contains the power changes of all motors and the gradients that affect the total power demand. Each motor i has a priority p(i), where p(i) represents the power change and gradient. The motor selection based on the priority queue can be implemented by the following steps:

[0052] Step S31, the power changes and gradients of all motors are calculated and inserted into the priority queue PQ in descending order of priority.

[0053] Step S32, initializing a variable best_p to record the current minimum priority value; initializing a variable selected_motor to record the selected motor.

[0054] Step S33, repeat the following steps a to c until the priority queue PQ is empty:

[0055] Step a, delete the element with the highest priority from PQ, that is, motor i with the largest p(i);

[0056] Step b, if p(i) is less than best_p, update best_p and selected_motor to p(i) and i respectively;

[0057] Step c, insert element i at the end of PQ.

[0058] Step S34, returning selected_motor, which is the motor with the smallest impact on the total power demand.

[0059] In this way, the motor with the least impact on the total power demand can be quickly found, thus achieving smooth power distribution. The advantage of this method is that it can dynamically adjust the selection order of the motor according to the actual situation, so that the system can better adapt to the real-time changing power demand.

[0060] In step S4, a PID controller may be used to adjust the power output of the motor. The PID controller is a control algorithm that can quickly and accurately adjust the output of the system. By adjusting the power output of the motor by the PID controller, smooth power distribution can be quickly achieved.

[0061] The PID controller consists of three parts: proportional (P), integral (I) and differential (D). The proportional part is used to adjust the proportional relationship between the output and input of the system; the integral part is used to eliminate the error of the system; and the differential part is used to improve the response speed of the system.

[0062] Assume there is a motor m with a power output of P m (t), the target power is P t (t). Then the calculation formula of the PID controller can be expressed as:

[0063] ΔP m (t) = K p (P t (t)-P m (t))+K i ∫(P t (t)-P m (t))dt+K d (P t (t)-P m (t)) / dt,

[0064] in:

[0065] ΔP m (t) is the power change of motor m;

[0066] K p , K i and K d are the three parameters of the PID controller, representing the proportional coefficient, integral coefficient and differential coefficient respectively;

[0067] P t (t) and P m (t) are the target power and the actual power output of motor m, respectively;

[0068] ∫(P t (t)-P m (t))dt represents the integral of the error;

[0069] (P t (t)-P m (t)) / dt represents the differential of the error.

[0070] By adjusting the parameter K of the PID controller p , K i and Kd , can realize the fast and accurate adjustment of the motor power output, thus realizing the smooth distribution of power. In practical applications, the parameter value of the PID controller can also be determined by experiments or system identification methods.

[0071] In summary, the method for synthesizing gravity energy storage multi-motor grid-connected based on the greedy algorithm of the present invention is considered from the perspective of power engineering application, and a method for smooth power synthesis of multiple motors in a gravity energy storage system is designed, which is helpful to promote the development and popularization of energy storage technology. By optimizing the operating state of the motor to effectively reduce unnecessary energy waste, the smooth power synthesis of multiple motors can ensure the power output stability of the system under various working conditions and improve the overall efficiency of the energy storage system. The coordinated work of multiple motors can enhance the stability of the entire energy storage system, so that it can still maintain a high operating efficiency in the face of various emergencies. Through the greedy algorithm, a simple and effective control logic can be implemented to reduce the control complexity of the energy storage system. Due to the use of advanced control strategies, this method can significantly improve the operating efficiency of the energy storage system, thereby reducing operating costs.

Claims

1. A method for synthesizing gravity energy storage multi-motor grid connection based on a greedy algorithm, characterized in that: It includes the following steps: Step S1: Initialization, setting the total power demand and the current power output value of each motor; Step S2: Calculate the impact of increasing or decreasing the power of each motor on the total power demand based on the total power demand and the current power output value of each motor; Step S3: Select a motor whose power change has the smallest impact on the total power demand; Step S4: adjusting the power output according to the selected motor, reducing the fluctuation of the total power output curve by increasing or decreasing the output power of the motor, and updating the total power demand; Step S5: repeating steps S2 to S4 until the total power demand is met or all motors have been considered; Wherein, step S2 includes: Assume that the function f(x1,x2,...,xn) represents the total power demand, where xi is the power output of the i-th motor; The gradient of a function represents the rate of change of the function value with respect to each variable, and is represented by a vector: Iterate the search in the opposite direction of the gradient to find a point where the function value decreases. Each iteration is performed according to the following formula: Among them, α is the learning rate, which determines the size of each step; through the process of continuous iteration, we can eventually find a motor that has the least impact on the total power demand; Step S3 includes assuming a priority queue PQ, which contains the power changes of all motors and the gradients affecting the total power demand, and each motor i has a priority p(i), where p(i) represents the power change and the gradient, and then the motor selection based on the priority queue is implemented through the following steps: Step S31, calculate the power changes and gradients of all motors, and insert them into the priority queue PQ in descending order of priority; Step S32, initializing a variable best_p for recording the current minimum priority value; initializing a variable selected_motor for recording the selected motor; Step S33, repeat the following steps a to c until the priority queue PQ is empty: Step a, delete the element with the highest priority from PQ, that is, motor i with the largest p(i); Step b, if p(i) is less than best_p, update best_p and selected_motor to p(i) and i respectively; Step c, insert element i at the end of PQ; Step S34, returning selected_motor, which is the motor with the smallest impact on the total power demand; In step S4, a PID controller is used to adjust the power output of the motor: The PID controller consists of three parts: proportional, integral and differential. The proportional part is used to adjust the proportional relationship between the output and input of the system; the integral part is used to eliminate the error of the system; the differential part is used to improve the response speed of the system. Assume a motor m, whose power output is Pm(t) and target power is P t (t), the calculation formula of the PID controller is expressed as: △P m (t)=K p (P t (t)-P m (t))+K i ∫(P t (t)-P m (t))dt+K d (P t (t)-P m (t)) / dt, Among them, ΔP m (t) is the power change of motor m, K p , K i and K d They are the proportional coefficient, integral coefficient and differential coefficient of the PID controller respectively; By adjusting the parameter K of the PID controller p , K i and K d , which can realize fast and accurate adjustment of motor power output, thereby achieving smooth power distribution.

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