Gravity energy storage multi-machine system mass block dynamic distribution method and device and electronic equipment

Abstract

The invention discloses a gravity energy storage multi-machine system mass block dynamic distribution method and device and electronic equipment, and the distribution method comprises the steps: calculating a loss function of an energy storage unit, the loss function being a quadratic function about the output power of a mass block; determining a quantity constraint condition of the mass blocks according to the power required to be output by the energy storage system and the rated power of the energy storage unit; and determining an optimal allocation method of the mass blocks according to a Monte Carlo algorithm and the loss function of the energy storage unit under the condition of quantity constraint of the mass blocks. The number of the mass blocks in different energy storage units can be dynamically distributed in real time, the energy efficiency is improved, and the loss of the gravity energy storage multi-machine system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage, and in particular, to a method, device, and electronic device for dynamically allocating mass blocks in a multi-machine system of gravity energy storage. Background Art

[0002] With the increasing global demand for renewable energy, energy storage technology plays an increasingly prominent role in balancing power supply and demand and improving power grid stability. The vertical multi-machine system of gravity energy storage has become a research hotspot due to its high energy density, environmental friendliness, and other characteristics. However, in the case of multi-machine collaborative work, inappropriate allocation of the number of mass blocks between different energy storage units may lead to an increase in mechanical and electrical losses and reduce the overall efficiency of the system.

[0003] The existing multi-machine mass block allocation strategies generally rely on average allocation or simple rules, ignoring the differential impact of losses in the multi-machine (i.e., multiple energy storage units) system, and it is difficult to operate with an optimal scheme under complex operating conditions. Summary of the Invention

[0004] Based on this, the present invention proposes a method, device, and electronic device for dynamically allocating mass blocks in a multi-machine system of gravity energy storage to better allocate mass blocks, improve energy efficiency, and reduce losses.

[0005] In a first aspect, an embodiment of the present invention provides a method for dynamically allocating mass blocks in a multi-machine system of gravity energy storage. The method includes: S102: calculating a loss function of an energy storage unit, where the loss function is a quadratic function of the output power of the mass block; S104: determining a quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit; S106: determining an optimal allocation method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit under the quantity constraint condition of the mass block.

[0006] Further, in S104, the total number n 总 of all mass blocks in the energy storage system is calculated as follows: where P ref is the power required to be output by the energy storage system, m is the mass of a single mass block, v ref is the descending speed of the mass block, and n 总 is an integer; where P r is the rated power of the energy storage unit, n imax is the total number of mass blocks in the i-th energy storage unit, and n imax is an integer; the quantity constraint condition of the mass block is: n1 + n2 + ··· + n i = n 总 ; n i ≤ n imax ; where n1, n2, ni The number of mass blocks in the 1st, 2nd, and i-th energy storage units respectively, n1, n2, n i are integers.

[0007] Furthermore, S106 includes: S1062: Based on the Monte Carlo algorithm, assign time-related random integers of mass blocks to each energy storage unit, and determine the total loss of the current energy storage system based on the random integers and the loss function, where the random integers of mass blocks satisfy the quantity constraint conditions of the mass blocks; S1064: Execute the Monte Carlo algorithm loop, and determine the optimal allocation quantity of mass blocks in the energy storage unit based on preset conditions.

[0008] Furthermore, in step S106, according to the loss functions of different energy storage systems and different quantity constraint conditions of mass blocks, dynamically confirm the optimal allocation quantity of mass blocks in each energy storage unit.

[0009] Furthermore, in S102, the loss ΔP of any one energy storage unit i The calculation method is: ΔP i = ∑P B + ∑P G + ∑P F + P N + P X + P Y ; where P B , P G , P F , P N , P X , P Y are respectively the friction loss between a single bearing and the rotating shaft, the meshing loss between any two gears, the friction loss of the belt drive, the power loss, the variable loss of the generator, and the constant loss of the generator, and the subscript i represents the i-th energy storage unit.

[0010] Furthermore, the calculation method of the friction loss P between a single bearing and the rotating shaft B is: where T a is the friction torque generated by the axial load of the bearing, n 轴 is the rotational speed of the bearing, T 机 is the torque of the motor, r1 is the transmission ratio between the rotating shaft and the motor, P i is the output power of all mass blocks in the i-th energy storage unit; and the calculation method of the meshing loss P between any two gears G is where μ m is the gear meshing friction coefficient, T1 is the gear torque, n 齿轮 is the rotational speed of the gear, βw is the working helix angle of the gear, and M is the mechanical benefit coefficient of gear meshing.

[0011] Furthermore, the frictional loss P of the belt drive F : Among them, Among them, F0 is the initial tension of the belt, q is the mass per unit length of the belt, v, v1, and v2 are the belt speed, the surface speed of the driving pulley, and the surface speed of the driven pulley respectively, α1 is the central angle corresponding to the arc with elastic slip, E is the elastic modulus of the belt, A is the cross-sectional area of the belt, f is a mathematical constant and complex number, ω F is the angular velocity of the pulley, and r0 is the radius of the pulley; and Among them, I1 and I0 are the load current and no-load current respectively, R K 、R m are the short-circuit resistance and field resistance respectively, u is the voltage, is the power factor.

[0012] Furthermore, the loss ΔP of any energy storage unit i The calculation method is: Among them, c is the number of bearings in the energy storage unit, and t is the number of gears in the energy storage unit.

[0013] In a second aspect, an embodiment of the present invention provides a mass block dynamic distribution device for a gravity energy storage multi-machine system. The device includes a first distribution module for calculating a loss function of the energy storage unit, where the loss function is a quadratic function of the output power of the mass block; a second distribution module for determining the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit; and a third distribution module for determining the optimal distribution method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit under the quantity constraint condition of the mass block.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the above-mentioned mass block dynamic distribution method for the gravity energy storage multi-machine system.

[0015] The beneficial effects of the embodiments of the present invention are as follows:

[0016] The present invention discloses a method, device, and electronic device for dynamically allocating mass blocks in a multi-machine system for gravity energy storage. The allocation method includes: calculating a loss function of an energy storage unit, where the loss function is a quadratic function of the output power of the mass block; determining a quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit; and determining an optimal allocation method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit. The present invention can dynamically allocate the number of mass blocks in different energy storage units in real time, improve energy efficiency, and reduce the loss of the multi-machine system for gravity energy storage. Description of the Drawings

[0017] Figure 1 It is a flowchart of a method for dynamically allocating mass blocks in a multi-machine system for gravity energy storage;

[0018] Figure 2 It is a schematic diagram of a multi-machine system for gravity energy storage;

[0019] Figure 3 It is a flowchart of another method for dynamically allocating mass blocks in a multi-machine system for gravity energy storage;

[0020] Figure 4 It is a schematic diagram of a loss characteristic curve;

[0021] Figure 5 It is a schematic diagram of a curve showing the optimal configuration of the mounted mass blocks for three energy storage units under different mass block requirements solved by the Monte Carlo method;

[0022] Figure 6 It is a schematic diagram of a configuration curve of mass blocks for three energy storage units using the conventional average allocation method;

[0023] Figure 7 It is a schematic diagram of a comparison between the loss of a multi-machine system for gravity energy storage optimized by the Monte Carlo method and the loss of a multi-machine system for gravity energy storage using the conventional method;

[0024] Figure 8 It is a schematic diagram of a comparison between the efficiency of a multi-machine system for gravity energy storage optimized by the Monte Carlo method and the efficiency of a multi-machine system for gravity energy storage using the conventional method;

[0025] Figure 9 It is a schematic diagram of an electronic device. Detailed Embodiments

[0026] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0027] The present invention is applied to the dynamic allocation scenario of the number of mass blocks in the energy storage unit of a vertical multi-machine system for gravity energy storage.

[0028] Embodiment 1

[0029] To solve the problems of increased losses and decreased efficiency caused by uneven mass block distribution among multiple machines in the prior art, an embodiment of the present invention proposes a method for mass block distribution based on the Monte Carlo method. By optimizing the mass block distribution strategy for multiple machines, the comprehensive minimization of mechanical and electrical losses is achieved, while the operating efficiency of the system is improved.

[0030] As Figure 1 shown in the flowchart of the dynamic mass block distribution method for the gravity energy storage multi-machine system of the present invention, Figure 2 is a schematic diagram of a gravity energy storage multi-machine system provided by the present invention.

[0031] S102: Calculate the loss function of the energy storage unit, where the loss function is a quadratic function of the output power of the mass block.

[0032] Specifically, the gravity energy storage multi-machine system involved in the present invention can be a matrix-type, vertical, gravity multi-machine energy storage system. As Figure 2 shown, the system includes multiple energy storage units, and each energy storage unit includes 1 motor and multiple mass blocks. The number of mass blocks in each energy storage unit can be the same or different. The motor consumes energy when lifting the mass blocks and stores the input power in the form of potential energy. When power needs to be released, the mass blocks fall under the action of gravity to drive the motor to operate, and the gravitational potential energy is converted back into electrical energy. Since each mass block in this system has a fixed number of layers for stacking at the high potential level and the low potential level respectively, the electrical energy stored in all mass blocks can be ensured to be consistent.

[0033] Specifically, when calculating the loss of the energy storage unit, mechanical losses, electrical losses (i.e., power losses) need to be considered. Mechanical losses include losses related to the load and losses unrelated to the load, etc. Electrical losses include variable losses and immutable losses, etc. The present invention takes almost all dimensionless quantities that can affect losses into account and performs adaptive transformation. The loss of the energy storage unit obtained is a quadratic function of the output power of the mass block, and subsequent calculations are based on this quadratic function.

[0034] S104: Determine the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit.

[0035] Specifically, the present invention can determine the total number of mass blocks required according to the power required to be output by the energy storage system, and then confirm the maximum number of mass blocks that can be arranged in each energy storage unit according to the rated power of the energy storage unit, and use this as a constraint condition for calculating the loss.

[0036] S106: Under the quantity constraint condition of the mass block, determine the optimal distribution method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit.

[0037] Specifically, the present invention can solve integer programming multiple times through the Monte Carlo algorithm, use Matlab to allocate time-related random numbers, and set the optimization objective function; set the number of iterations or set the iteration conditions, perform the Monte Carlo method loop, and obtain the optimal number of mass block allocations for each energy storage unit; apply the optimization result to the multi-machine control system, and dynamically adjust the mass blocks hung on each energy storage unit in real time according to the power required by the power grid.

[0038] More specifically, during the operation of the multi-machine system of gravity energy storage, the power required by the system will change in real time, and the losses of different energy storage units are also different. The present invention can adjust the number of mass blocks according to the actual situation, that is, it can dynamically confirm the optimal allocation number of mass blocks in each energy storage unit according to the loss functions of different energy storage systems and the quantity constraint conditions of different mass blocks.

[0039] The present invention discloses a method, device and electronic equipment for dynamically allocating mass blocks in a multi-machine system of gravity energy storage. The allocation method includes: calculating the loss function of the energy storage unit, where the loss function is a quadratic function about the output power of the mass block; determining the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit; under the quantity constraint condition of the mass block, determining the optimal allocation method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit. The present invention can dynamically allocate the number of mass blocks in different energy storage units in real time, improve the energy efficiency, and reduce the losses of the multi-machine system of gravity energy storage.

[0040] Embodiment 2

[0041] The embodiment of the present invention provides a more detailed method for dynamically allocating mass blocks in a multi-machine system of gravity energy storage.

[0042] S102: Calculate the loss function of the energy storage unit, specifically including:

[0043] ΔP i = ∑P B + ∑P G + ∑P F + P N + P X + P Y Formula 1;

[0044] Among them, ΔP i 、P B 、P G 、P F 、P N 、P X 、P YThey are respectively the loss of any energy storage unit, the frictional loss between a single bearing and the rotating shaft, the meshing loss between any two gears, the frictional loss of the belt drive, the power loss, the variable loss of the generator, and the constant loss of the generator. The subscript i represents the i-th energy storage unit.

[0045] ∑P B 、∑P G 、∑P F The sum is P L and is the mechanical loss related to the load, that is, the bearing frictional loss.

[0046] P L 、P N The sum is P M and is the total mechanical loss.

[0047] P X 、P Y The sum is the electrical energy loss.

[0048] 1)

[0049] Among them, T a is the frictional torque generated by the axial load of the bearing, n 轴 is the rotational speed of the bearing, T 机 is the torque of the motor, r1 is the transmission ratio between the rotating shaft and the motor, and P i is the output power of all the mass blocks in the i-th energy storage unit.

[0050] Expand formula 2 in detail:

[0051]

[0052] Among them, T r is the frictional torque generated by the radial load of the bearing, and n 轴 is the rotational speed of the bearing.

[0053]

[0054] Among them, μ1 is the axial load friction coefficient, F1 is the axial force received by the bearing, and d m is the average diameter of the bearing.

[0055]

[0056] Among them, d i 、d o are the bore diameter and outer diameter of the bearing.

[0057]

[0058] Among them, μ2 is the radial load friction coefficient, F2 is the radial force received by the bearing, and a0 and b0 are correction coefficients.

[0059] Since Tr << Ta, it is approximately taken as:

[0060]

[0061] The relationship between the output power of the mass block (i.e., mechanical power) and the rotational speed of the motor

[0062] P0 is P i ;

[0063] Bearing transmission ratio

[0064] Combining Formula 7 - 9, Formula 2 can be obtained.

[0065] 2)

[0066] Among them, μ m is the gear meshing friction coefficient, T1 is the gear torque, n 齿轮 is the gear rotational speed, βw is the working helix angle of the gear, and M is the mechanical efficiency coefficient of gear meshing.

[0067] Expand Formula 8 in detail:

[0068]

[0069] The relationship between the output power of the mass block (i.e., mechanical power) and the rotational speed of the motor

[0070] P0 is P i ;

[0071] Gear transmission ratio

[0072] Combining Formulas 11 - 13, Formula 10 can be obtained.

[0073] 3)

[0074] Among them,

[0075] Among them, F0 is the initial tension of the belt, q is the mass per unit length of the belt, v, v1, and v2 are the belt speed, the surface speed of the driving pulley, and the surface speed of the driven pulley respectively, α1 is the central angle corresponding to the arc with elastic slip, E is the elastic modulus of the belt, A is the cross - sectional area of the belt, f is a mathematical constant, complex number, ω F is the angular velocity of the pulley, and r0 is the radius of the pulley.

[0076] Expand Formula 14:

[0077] The relationship between the output power of the mass (i.e., mechanical power) and the pulley: P0 = F0 × ω F × r0 Formula 17

[0078] Wherein, P0 is the output power of the mass in the energy storage unit, i.e., P i , ω F is the angular velocity of the pulley, and r0 is the radius of the pulley.

[0079] Combining Formulas 16 - 17, Formula 14 - 15 can be obtained.

[0080] 4) The power loss P of the gravity energy storage multi - machine system independent of the load N is mainly composed of the wind resistance power loss, the oil stirring power loss, and the oil seal power loss of each component, i.e.:

[0081] P N = ∑P W + ∑P V + ∑P S Formula 18;

[0082] In the formula: ∑P W is the sum of the wind resistance power losses of each component, ∑P V is the sum of the oil stirring power losses of each component, and ∑P S is the sum of the oil seal power losses of each component. In the quadratic function of the loss, P N is the constant term.

[0083] The electric energy loss is divided into a variable loss P X that changes with the size of the mass and a constant loss P Y that does not change with the size of the mass.

[0084] 5)

[0085] 6)

[0086] P X is the variable loss of the synchronous motor - generator, and P Y is the constant loss of the synchronous motor - generator.

[0087] The variable loss P X of the synchronous motor - generator is generally specified as the copper loss of the rotor winding, ignoring the change in the terminal voltage of the synchronous motor - generator. The constant loss of the synchronous motor - generator is mainly the hysteresis loss and eddy current loss in the armature, represented by P Y .

[0088]

[0089] Among them, I1 and I0 are the load current and no-load current respectively, and R K , R m are the short-circuit resistance and excitation resistance respectively, u is the voltage of the motor in the energy storage unit, is the power factor of the motor in the energy storage unit.

[0090] 7) Calculate the loss function of any one energy storage unit:

[0091]

[0092]

[0093] Among them, c is the total number of rotating shafts and bearings in the energy storage unit, and t is the number of gears in the gearbox of the energy storage unit.

[0094] Formula 22 is the loss function. Based on Formulas 1 - 22, it can be obtained that the loss function is a quadratic function of the output power P i of the mass block.

[0095] Formula 22 can be simplified to

[0096] Specifically, since the output mechanical power P i of all mass blocks of any one energy storage unit = n'mgv ref ; Formula 24

[0097] Among them, n' is the number of mass blocks, m is the mass of a single mass block, and v ref is the descending speed of the mass block. Formula 23 can be rewritten as:

[0098]

[0099] n i is the number of mass blocks mounted in any one energy storage unit.

[0100] The optimization objective function can be written in the following form: minΔP = ∑ΔP i Formula 26.

[0101] S102 constructs an optimization model whose goal is to minimize the total loss of the energy storage unit, including mechanical loss and electrical loss. In this process, the loss of the energy storage unit is related to the input mechanical power. By measuring the loss data at different input mechanical powers and using the least squares method to fit the loss characteristic curve of each energy storage unit, the mathematical model of the energy storage unit loss can be obtained.

[0102] S104: Determine the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit.

[0103]

[0104] Among them, P ref is the power required to be output by the energy storage system, m is the mass of a single mass block, and v ref is the descending speed of the mass block, and n 总 is the total number of all mass blocks in the energy storage system, and n 总 is an integer;

[0105]

[0106] Among them, P r is the rated power of the energy storage unit, and n imax is the total number of mass blocks in the i-th energy storage unit, and n imax is an integer;

[0107] The quantity constraint condition of the mass block is:

[0108] n1 + n2 + ··· + n i = n 总 Formula 29;

[0109] n i ≤ n imax Formula 30;

[0110] Among them, n1, n2, n i are respectively the number of mass blocks in the 1st, 2nd, and i-th energy storage units, and n1, n2, n i are integers.

[0111] For S104, define the constraint conditions: The constraint conditions mainly include: The number of mass blocks hung on each energy storage unit cannot exceed the maximum number of mass blocks allowed by the rated power. The working state of each energy storage unit needs to meet the power demand of the power grid.

[0112] S106: Under the quantity constraint condition of the mass block, determine the optimal allocation method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit.

[0113] S1062: Allocate time-related random integers of mass blocks to each energy storage unit based on the Monte Carlo algorithm, and determine the total loss of the current energy storage system based on the random integers and the loss function, where the random integers of the mass blocks satisfy the quantity constraint condition of the mass blocks;

[0114] 1064: Execute the Monte Carlo algorithm loop, and determine the optimal allocation quantity of the mass blocks in the energy storage unit based on the preset conditions.

[0115] Specifically, allocating quality block random integers related to time can avoid generating duplicate data, avoid duplicate calculations, and save computing power. In S106, all possible allocation schemes can be looped through and the optimal scheme can be obtained after comparison. Alternatively, a loss threshold can be preset, and the loop stops and the scheme is output when the loss value of the current allocation scheme ≤ the loss threshold.

[0116] For S106, specifically, the input mechanical power is changed multiple times, and the relationship between the input mechanical power of a single energy storage unit (the power of all the quality blocks of this energy storage unit added together) and the loss is measured. The loss characteristic curve of each energy storage unit (with different coefficients for different energy storage units) is obtained using the least squares method.

[0117] The number of iterations of the Monte Carlo method is dynamically adjusted according to the system power demand to balance the calculation efficiency and optimization accuracy.

[0118] In the present invention, for the problem of quality block allocation in a vertical gravity energy storage multi-machine system, the Monte Carlo method is used to solve the integer programming optimization problem to minimize the loss of the energy storage unit and achieve precise power regulation. The Monte Carlo method is a numerical calculation method based on probability statistics, which approximates the optimal solution through random sampling and a large number of experiments.

[0119] As a stochastic optimization algorithm, the Monte Carlo method has the superiority in solving complex non-linear problems. Therefore, developing a quality block allocation method for a vertical gravity energy storage multi-machine system by combining the Monte Carlo method is of great significance for improving the operating performance of the gravity energy storage multi-machine system. The applications of Monte Carlo include the following:

[0120] 1) Problem modeling and objective function

[0121] First, an optimization model is constructed. The goal of this model is to minimize the total loss of the energy storage unit, including mechanical loss and electrical loss. In this process, the loss of the energy storage unit is related to the input mechanical power. By measuring the loss data at different input mechanical powers, we use the least squares method to fit the loss characteristic curve of each energy storage unit, and then obtain the mathematical model of the energy storage unit loss.

[0122] 2) Random sampling and search of the Monte Carlo method

[0123] The core idea of the Monte Carlo method is to search for the optimal solution through a large number of random samplings. The specific steps are as follows: Initialization: First, generate the number of mass block allocations for each energy storage unit and calculate the initial total loss of the system. Random Sampling: Use MATLAB to allocate random numbers (number of mass blocks) related to time, simulate different mass block allocation schemes, and calculate the loss value for each scheme. Optimization Objective: The optimization objective is to minimize the total loss function of all energy storage units. Each time a sample is taken, we calculate the loss characteristics of all energy storage units and obtain the current total loss. Iterative Update: After each random sampling, we compare the current loss value with the historical optimal solution. If the current loss is smaller, we update the optimal solution.

[0124] 3) Iterative Search and Convergence

[0125] The Monte Carlo method gradually approaches the optimal solution through multiple iterative searches. In each loop, we adjust the number of mass blocks of the energy storage unit and dynamically adjust it according to the power demand of the power grid until the total loss of the system converges to the optimal value.

[0126] 4) Result Output and Application

[0127] Finally, the optimal mass block allocation scheme obtained by the Monte Carlo method will be applied to the multi-machine control system. The number of mass blocks of each energy storage unit is dynamically adjusted according to the real-time power demand of the power grid, so as to ensure accurate response to the change of the power grid mass block while minimizing the loss.

[0128] In addition, S106 includes:

[0129] S1061. Obtain the operating parameters of the energy storage system and establish a mathematical model of mechanical loss and electrical loss;

[0130] S1062. Set the optimization objective function and constraint conditions with the goal of minimizing the total system loss;

[0131] S1063. Input the total system power demand, generate random load allocation samples using the Monte Carlo method, set the sampling number and iteration number of the Monte Carlo method, and calculate the total loss value of each sample;

[0132] S1064. Optimize the load allocation scheme according to the loss value ranking until the optimal scheme is obtained;

[0133] S1065. Apply the optimization result to the multi-machine control system and dynamically adjust the operating state of each energy storage unit

[0134] The beneficial technical effects of the present invention are as follows:

[0135] The optimization method implemented by the Monte Carlo method can adapt to various operating conditions of complex nonlinear systems, ensure stable optimization results, and this method does not require complex hardware modification. It only needs to introduce an optimization algorithm into the existing control system, and has the characteristics of high efficiency, flexibility and easy engineering application.

[0136] As Figure 3 shown, the present invention discloses a vertical gravity energy storage multi-machine mass block allocation method based on the Monte Carlo method, aiming to optimize the operating efficiency of the energy storage system and reduce energy loss. This method establishes a mathematical model of mechanical loss and electrical loss of the energy storage system, takes the minimum total loss of the system as the goal, uses the Monte Carlo method to generate random mass block allocation samples, calculates the loss values of each sample, and iteratively optimizes the mass block allocation scheme to finally obtain the optimal allocation result. During the optimization process, the constraint conditions of the operating parameters of the energy storage unit and the system power demand are fully considered, and the mass block allocation is dynamically adjusted to adapt to different operating scenarios. The present invention can improve the overall operating efficiency of the gravity energy storage multi-machine system, and at the same time the method is easy to implement, does not require complex hardware modification, has high engineering applicability, and is applicable to a variety of energy storage application scenarios.

[0137] The purpose of the present invention is to determine how to schedule and allocate the power output of each energy storage unit when the required output power (i.e., Pref) of the gravity energy storage multi-machine system is certain, so as to make the whole system have the highest efficiency and the minimum total loss. According to the total required output power of the system, random numbers are generated by the Monte Carlo method and substituted into the loss characteristic function of each energy storage unit, and the total loss of the system is calculated by summation. This process is looped to find the result with the minimum total loss, obtain the optimal power mass block allocation of each energy storage unit, and then obtain the optimal allocation result of the mass block of each energy storage unit.

[0138] This embodiment provides a vertical gravity energy storage multi-machine mass block allocation method based on the Monte Carlo method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0139] Demonstration process:

[0140] Taking a 1.1kW vertical gravity energy storage multi-machine system as an example, the system has a total of three energy storage units (one energy storage unit includes one motor). Each energy storage unit selects an asynchronous motor with a capacity of 1.1kW, and the maximum number of mass blocks that each motor is designed to allow to be hung is 12. The weight of each mass block is 3.9kg, and the total weight of auxiliary equipment such as lifting tools and lifting vehicles is 52.07kg.

[0141] First, the least squares method is used to fit the loss characteristics of each energy storage unit in the objective function, and the fitting results are as follows:

[0142] Energy storage unit 1:

[0143] Energy storage unit 2:

[0144] Energy storage unit 3:

[0145] The loss characteristic curves of energy storage unit 1, energy storage unit 2, and energy storage unit 3 are as Figure 4 shown. From Figure 4 it can be seen that although these three energy storage units have the same model, due to some possible errors in the manufacturing process and the influence of factors such as materials and processes, their loss characteristic curves show some slight differences. These differences may be manifested as different slopes, inflection point positions, and overall shapes of the curves, reflecting that the efficiency and energy consumption characteristics of each energy storage unit in actual operation are not exactly the same. Therefore, these differences must be fully considered to ensure the best operating efficiency and economy in actual applications.

[0146] As Figure 4 shown, the abscissa is the output power of all mass blocks in the energy storage unit, namely P1, P2, and P3, and the ordinate is the loss of the energy storage unit, namely ΔP1, ΔP2, and ΔP3. Under different mass block requirements, the Monte Carlo method is used to solve the integer programming problem, and the mass blocks mounted on each energy storage unit are obtained respectively. The number of mounted mass blocks that minimizes the sum of the losses of each energy storage unit is obtained, and the optimal mounted mass block configuration of each energy storage unit under different mass block requirements is obtained. The solution results (i.e., the number of mounted mass blocks of each energy storage unit after optimization) are as Figure 5 shown, Figure 6 which is the mounting situation of the mass blocks by the conventional average distribution method.

[0147] Figure 5 , Figure 6 in which the abscissa represents the total number of mass blocks required by the energy storage system, and the ordinate represents the number of mass blocks allocated to each energy storage unit. It can be seen that the optimization algorithm of this application tends to allocate more mass blocks to the energy storage unit with higher current efficiency.

[0148] The loss power of the optimized gravity energy storage multi-machine system is as Figure 7 shown, and the efficiency of the multi-machine system is as Figure 8 shown. Figure 7 The abscissa represents the total number of mass blocks required, and the ordinate is the total loss power of the multi-machine system (i.e., ΔP1 + ΔP2 + ΔP3); Figure 8 The abscissa is the total number of mass blocks required, and the ordinate represents the efficiency η. Here,

[0149] From Figures 7 - 8It can be seen that compared with the average distribution scheme, the overall efficiency of the system after Monte Carlo optimization has been improved, with the maximum efficiency increased by 0.25% and the average efficiency increased by 0.1%.

[0150] Although simply relying on the distribution algorithm has limited effect on improving the energy efficiency of the system, in large-scale energy storage systems, even a small efficiency improvement, in large-capacity energy storage applications, its cumulative effect will be significantly converted into economic benefits, which helps to further improve the overall performance and economy of the system.

[0151] The present invention significantly reduces mechanical and electrical losses through the optimal mass block distribution strategy, reduces the total loss, improves the conversion efficiency, and significantly improves the system operation efficiency in high-power demand scenarios.

[0152] Embodiment 3

[0153] The present embodiment provides a mass block dynamic distribution device for a gravity energy storage multi-machine system, characterized in that the device includes:

[0154] A first distribution module for calculating the loss function of the energy storage unit, where the loss function is a quadratic function of the output power of the mass block.

[0155] A second distribution module for determining the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit.

[0156] A third distribution module for determining the optimal distribution method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit under the quantity constraint condition of the mass block.

[0157] The beneficial effects of the mass block dynamic distribution device for the gravity energy storage multi-machine system provided by the present invention are the same as those of the embodiments of the mass block dynamic distribution method for the gravity energy storage multi-machine system, and will not be elaborated in this embodiment.

[0158] Embodiment 4

[0159] As Figure 9 shown, the embodiment of the present invention also provides an electronic device 130, which is a schematic structural diagram of the electronic device 130 provided by the embodiment of the present invention, including: a processor 131, a memory 132, and a bus 133. The memory 123 stores machine-readable instructions executable by the processor 131. When the electronic device 130 runs, the processor 131 communicates with the memory 123 through the bus 133, and the machine-readable instructions are executed by the processor 131 to perform the methods described in the above method embodiments.

[0160] Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the above-mentioned method for dynamically allocating mass blocks in a gravity energy storage multi-machine system. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described system and device can refer to the corresponding processes in the method embodiments, and will not be elaborated herein in the present invention. In several embodiments provided by the present invention, it should be understood that the disclosed system, device, and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the device or module can be in an electrical, mechanical, or other form. In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, and other various media that can store program codes.

[0161] The above embodiments are only examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. The obvious changes or variations derived therefrom still fall within the protection scope of the present invention.

Claims

1. A method for dynamically allocating mass blocks in a gravity energy storage multi-machine system, characterized in that, The method includes: S102: Calculate the loss function of the energy storage unit, where the loss function is a quadratic function of the output power of the mass block; S104: Determine the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit; S106: Under the quantity constraint condition of the mass block, determine the optimal allocation method of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit.

2. The method for dynamically allocating mass blocks in a gravity energy storage multi-machine system according to claim 1, wherein In S104, the total number n of all mass blocks in the energy storage system 总 is calculated as follows: Among them, P ref is the power required to be output by the energy storage system, m is the mass of a single mass block, v ref is the descending speed of the mass block, n 总 is an integer; Among them, P r is the rated power of the energy storage unit, n imax is the total number of mass blocks in the i-th energy storage unit, n imax is an integer; The quantity constraint condition of the mass block is: n1 + n2 + ··· + n i = n 总 ; n i ≤ n imax ; where n1, n2, and n i are the numbers of mass blocks in the 1st, 2nd, and i-th energy storage units respectively, and n1, n2, and n i are integers.

3. The method for dynamically allocating mass blocks in a gravity energy storage multi-machine system according to claim 2, wherein, S106 includes: S1062: Allocate time-related random integers of the mass block to each energy storage unit based on the Monte Carlo algorithm, and determine the total loss of the current energy storage system based on the random integer and the loss function, where the random integer of the mass block satisfies the quantity constraint condition of the mass block; S1064: Execute the Monte Carlo algorithm loop, and determine the optimal allocation quantity of the mass block in the energy storage unit based on a preset condition.

4. The method for dynamically allocating mass blocks of the gravity energy storage multi-machine system according to claim 1, wherein In step S106, according to the loss functions of different energy storage systems and different quantity constraint conditions of the mass block, dynamically confirm the optimal allocation quantity of the mass block in each energy storage unit.

5. The method for dynamically allocating mass blocks in a gravity energy storage multi-machine system according to claim 1, characterized in that In S102, the loss ΔP of any energy storage unit i The calculation method is as follows: ΔP i = ∑P B + ∑P G + ∑P F + P N + P X + P Y ; Among them, P B , P G , P F , P N , P X , P Y are respectively the frictional loss between a single bearing and the rotating shaft, the meshing loss between any two gears, the frictional loss of the belt drive, the power loss, the variable loss of the generator, and the constant loss of the generator. The subscript i represents the i-th energy storage unit.

6. The method for dynamically allocating mass blocks in a gravity energy storage multi-machine system according to claim 5, characterized in that The frictional loss P between a single bearing and the rotating shaft B is calculated as follows: Among them, T a is the frictional torque generated by the axial load of the bearing, n 轴 is the rotational speed of the bearing, T 机 is the torque of the motor, r1 is the transmission ratio between the rotating shaft and the motor, P i is the output power of all the mass blocks in the i-th energy storage unit; and the meshing loss P G between any two gears is calculated as follows: Among them, μ m is the gear meshing friction coefficient, T1 is the gear torque, n 齿轮 is the gear rotational speed, βw is the working helix angle of the gear, and M is the mechanical benefit coefficient of gear meshing.

7. The method for dynamically allocating mass blocks of the gravity energy storage multi-machine system according to claim 6, wherein Belt drive friction loss P F : Among them, Among them, F0 is the initial tension of the belt, q is the mass per unit length of the belt, v, v1, and v2 are the belt speed, the surface speed of the driving pulley, and the surface speed of the driven pulley respectively, α1 is the central angle corresponding to the arc with elastic slip, E is the elastic modulus of the belt, A is the cross-sectional area of the belt, f is a mathematical constant and a complex number, ω F is the angular velocity of the pulley, r0 is the radius of the pulley; and where I1 and I0 are the load current and no-load current respectively, and R K , R m are the short-circuit resistance and field resistance respectively, u is the voltage, is the power factor.

8. The method for dynamically allocating mass blocks in a gravity energy storage multi-machine system according to claim 7, characterized in that, The loss ΔP of any energy storage unit i The calculation method is as follows: Where c is the number of bearings in the energy storage unit, and t is the number of gears in the energy storage unit.

9. A mass block dynamic distribution device for a gravity energy storage multi-machine system, characterized in that, The device includes: A first allocation module, configured to calculate the loss function of the energy storage unit, where the loss function is a quadratic function of the output power of the mass block; A second allocation module, configured to determine the quantity constraint condition of the mass block according to the power required to be output by the energy storage system and the rated power of the energy storage unit; A third allocation module, configured to determine the optimal allocation method of the mass block under the quantity constraint condition of the mass block according to the Monte Carlo algorithm and the loss function of the energy storage unit.

10. An electronic device, characterized in that, Includes: A processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus. The processor executes the machine-readable instructions to execute the dynamic allocation method of the mass block of the gravity energy storage multi-machine system as described in any one of claims 1 to 8.

Images