An online optimization method for operating efficiency of a multi-track slope gravity energy storage system

Abstract

The present invention discloses an online optimization method for the operating efficiency of a multi-track ramp gravity energy storage system, including: according to the charging and discharging instructions of the power grid, using a discrete particle swarm algorithm to calculate the control variables that maximize the overall charging efficiency and discharging efficiency of the gravity energy storage system; the constraint conditions of the ramp gravity energy storage system under the energy storage condition and the energy release condition; and the step of calculating the control variables using a discrete particle swarm algorithm. This method effectively overcomes the problem that the existing control strategy fails to improve the power efficiency by coordinating and optimizing the number of ramp track mass blocks and the gearbox ratio of the traction motor, thereby maximizing the power efficiency of the entire system while ensuring that the power grid dispatching instructions are met, and realizing the online optimization of the system operating efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of slope type gravity energy storage, in particular to an online optimization method for operating efficiency of a multi-track slope type gravity energy storage system. Background Art

[0002] With the continuous growth of global energy demand and the increasing prominence of environmental problems, the development and utilization of renewable energy has become increasingly important. However, due to the intermittent and unstable nature of renewable energy, its balance and dispatch with the power grid face huge challenges. Gravity energy storage technology has become a highly-regarded energy storage solution due to its advantages such as high efficiency, environmental protection and long life. The gravity energy storage system stores energy by lifting the mass block to a high place when there is excess electricity, and releases the mass block to generate electricity when the electricity demand peaks. The multi-track ramp gravity energy storage system has high flexibility and efficiency. Through the multi-track design, it can be flexibly dispatched under different grid dispatch requirements, improving the reliability and adaptability of the system.

[0003] At present, the control strategy of the gravity energy storage system does not consider the joint optimization control of the number of mass blocks on the slope track and the gearbox ratio of the traction motor while meeting the grid dispatching instructions, resulting in the system not operating at the overall highest efficiency. In order to improve the operating efficiency of the multi-track slope gravity energy storage system, there is an urgent need for a control strategy that can simultaneously optimize the number of mass blocks on the slope track and the gearbox ratio of the traction motor, and realize an online optimization method for the system operating efficiency. Summary of the invention

[0004] In view of the problem that the control strategy of the existing gravity energy storage system does not consider the joint optimization control of the number of mass blocks on the slope track and the gearbox ratio of the traction motor while satisfying the grid dispatching instructions, resulting in the system not operating at the overall highest efficiency, the present invention proposes an online optimization method for the operating efficiency of a multi-track slope gravity energy storage system.

[0005] The technical solution of the present invention is as follows: A method for online optimization of the operating efficiency of a multi-track slope gravity energy storage system, characterized in that: the slope gravity energy storage monitoring and energy management system is based on the power grid charging instruction P AGC_cha and discharge command P AGC_dis , the discrete particle swarm algorithm is used to calculate the overall charging efficiency η of the gravity energy storage system cha and discharge efficiency η dis The control variables to be maximized include the gearbox ratio k of the traction motor of each slope track i (i=1,2,...,n G , n G ∈Z) and the number of mass blocks on each ramp track n slope_i (i=1,2,...,n G, n G ∈Z), where i is the ramp track number, n G is the number of ramp tracks, used for online control of multi-track ramp gravity energy storage system;

[0006] The charging efficiency η of the slope type gravity energy storage system under the energy storage condition cha , specifically:

[0007]

[0008] Among them, η slope_cha_i is the mass moving efficiency of the i-th slope track under energy storage conditions, η chain_cha_i is the gear chain transmission efficiency of the i-th slope track under energy storage conditions, η gear_cha_i is the gearbox transmission efficiency of the i-th slope track under energy storage conditions, η G_cha_i is the traction motor efficiency of the i-th slope track under energy storage conditions, P G_cha_i is the input power of the traction motor of the i-th slope track under the energy storage condition;

[0009] The mass moving efficiency η of the i-th slope track under the energy storage condition slope_cha_i , whose expression is:

[0010]

[0011] Among them, θ is the inclination angle of the ramp track relative to the horizontal plane, and μ is the dynamic friction coefficient of the ramp track;

[0012] The gear chain transmission efficiency η of the i-th slope track under the energy storage condition chain_cha_i , whose expression is:

[0013]

[0014] Among them, n slope_i is the number of mass blocks on the i-th ramp track, m is the mass of a single mass block, g is the acceleration of gravity, ω is the angular velocity of the traction motor, r is the sprocket radius of the ramp track, k i is the gearbox ratio of the traction motor of the i-th slope track, P chain_loss Fixed losses for gear chain friction;

[0015] The gearbox transmission efficiency η of the i-th slope track under the energy storage condition gear_cha_i , whose expression is:

[0016]

[0017] Among them, T idle is the no-load torque of the gearbox, K is a constant;

[0018] The traction motor efficiency η of the i-th slope track under the energy storage condition G_cha_i , whose expression is:

[0019]

[0020] Among them, I i is the input current of the traction motor of the i-th slope track, R s is the stator winding resistance, I f_i is the excitation current of the traction motor on the i-th slope track, R f is the rotor winding resistance, P fe is the iron loss of the traction motor, is the mechanical loss of the traction motor;

[0021] The discharge efficiency η of the slope type gravity energy storage system under the energy release condition dis , specifically:

[0022]

[0023] Among them, η slope_dis_i is the mass moving efficiency of the i-th slope track under the energy release condition, η chain_dis_i is the gear chain transmission efficiency of the i-th slope track under the energy release condition, η gear_dis_i is the gearbox transmission efficiency of the i-th slope track under the energy release condition, η G_dis_i is the traction motor efficiency of the i-th slope track under the energy release condition, P G_dis_i is the output power of the traction motor of the i-th slope track under the energy release condition;

[0024] The mass moving efficiency η of the i-th slope track under the energy release condition slope_dis_i , whose expression is:

[0025]

[0026] Among them, θ is the inclination angle of the ramp track relative to the horizontal plane, and μ is the dynamic friction coefficient of the ramp track;

[0027] The gear chain transmission efficiency η of the i-th slope track under the energy release condition chain_dis_i , whose expression is:

[0028]

[0029] Among them, n slope_i is the number of mass blocks on the i-th ramp track, m is the mass of a single mass block, g is the acceleration of gravity, ω is the angular velocity of the traction motor, r is the sprocket radius of the ramp track, k i is the gearbox ratio of the traction motor of the i-th slope track, Pchain_loss Fixed losses for gear chain friction;

[0030] The gearbox transmission efficiency η of the i-th slope track under the energy release condition gear_dis_i , whose expression is:

[0031]

[0032] Among them, T idle is the no-load torque of the gearbox, K is a constant;

[0033] The traction motor efficiency η of the i-th slope track under the energy release condition G_dis_i , whose expression is:

[0034]

[0035] Among them, I i is the input current of the traction motor of the i-th slope track, R s is the stator winding resistance, I f_i is the excitation current of the traction motor on the i-th slope track, R f is the rotor winding resistance, P fe is the iron loss of the traction motor, Mechanical loss of traction motor.

[0036] As a preferred solution of the online optimization method for the operating efficiency of the multi-track slope gravity energy storage system of the present invention, the constraints of the slope gravity energy storage system under the energy storage condition include:

[0037] The traction motor input power P of the i-th slope track under the energy storage condition G _ cha _i satisfies:

[0038]

[0039] in,

[0040] The traction motor gearbox ratio k of the i-th slope track i satisfy:

[0041] k i ∈k n

[0042] Among them, k n Optional ratio for gearbox;

[0043] The number of mass blocks n on the i-th ramp track slope_i satisfy:

[0044] n slope_i ∈[0,nslope,max ](n slope_i ∈Z)

[0045] Among them, n slope，max is the maximum number of mass blocks on the ramp track.

[0046] As a preferred solution of the online optimization method for the operating efficiency of the multi-track slope gravity energy storage system of the present invention, the constraint conditions of the slope gravity energy storage system under the energy release condition include:

[0047] The output power P of the traction motor of the i-th slope track under the energy release condition is G_dis_i satisfy:

[0048]

[0049] in,

[0050] The traction motor gearbox ratio k of the i-th slope track i satisfy:

[0051] k i ∈k n

[0052] Among them, k n Optional ratio for gearbox;

[0053] The number of mass blocks n on the i-th ramp track slope_i satisfy:

[0054] n slope_i ∈[0,n slope,max ](n slope_i ∈Z)

[0055] Among them, n slope,max is the maximum number of mass blocks on the ramp track.

[0056] As a preferred solution of the online optimization method for the operating efficiency of the multi-track slope gravity energy storage system of the present invention, the step of calculating the control variables by the discrete particle swarm algorithm is:

[0057] S401: According to the power grid charging instruction P AGC_cha The slope gravity energy storage system operates in the energy storage condition. According to the charging efficiency η as claimed in claim 1 cha and the constraints under the energy storage condition as claimed in claim 2, the objective function is defined as:

[0058]

[0059] Where λ is the penalty factor;

[0060] According to the grid charging instruction P AGC_dis The slope type gravity energy storage system operates in the energy release condition. According to the discharge efficiency η as claimed in claim 1 dis and the constraints under the energy release condition as claimed in claim 3, the objective function is defined as:

[0061]

[0062] S402: Initialize variables:

[0063]

[0064] Initialize particle velocity:

[0065] v i =(v1, ..., v 2nG ) T

[0066] S403: Calculate the fitness of each particle:

[0067]

[0068] Determine the global optimum and the individual optimum for each particle;

[0069] S404: Update particle velocity:

[0070] v i (t+1)=ω·v i (t)+c1·ss(x i , p i )+c2·ss(x i , g i )

[0071] Among them, ω is the inertia weight, c1 and c2 are learning factors, and p i is the optimal position of individual particles, g i is the global optimal position of the particle, and the function ss(x, y) is the exchange function;

[0072] S405: Update particle position:

[0073] x i (t+1)=apply_operations(x i (t), v i (t+1))

[0074] S406: Determine whether each particle meets the constraint conditions, specifically:

[0075] Determine k i Whether it is within the optional ratio range;

[0076] Judgement slope_i Whether it is within the maximum number of masses allowed to move simultaneously on the ramp track;

[0077] judge Is it in the range of [0, 1]?

[0078] If there are particles that do not meet the constraint conditions, return to step S404;

[0079] S407: Calculate the new position fitness of each new particle:

[0080]

[0081] S408: Update individual optimality:

[0082] like

[0083] f(x i T )>f(p i T )

[0084] but

[0085] p i T =x i T

[0086] Update the global optimum:

[0087] like

[0088] f(x i T )>f(g i T )

[0089] but

[0090] g i T =x i T

[0091] S409: repeat S404 to S408 until the maximum number of iterations is met, and then stop iterating;

[0092] S4010: Return the optimal control variables to the ramp-type gravity energy storage monitoring and energy management system to execute the latest control strategy.

[0093] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0094] The present invention is based on the power grid charging instruction P AGC_cha and discharge command P AGC_dis, the discrete particle swarm algorithm is used to calculate the overall charging efficiency η of the gravity energy storage system cha and discharge efficiency η dis The control variables to be maximized include the gearbox ratio k of the traction motor on each slope track i (i=1,2,...,n G , n G ∈Z) and the number of mass blocks on each ramp track n slope_i (i=1,2,...,n G , n G ∈Z), which effectively overcomes the problem that the existing control strategy fails to improve the power efficiency by optimizing the number of slope track mass blocks and the gearbox ratio of the traction motor, thereby maximizing the power efficiency of the entire system while ensuring that the grid dispatching instructions are met, and realizing online optimization of the system operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0096] Figure 1 Schematic diagram of an online optimization method for operating efficiency of a multi-track slope gravity energy storage system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0097] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0098] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0099] For easier understanding, see Figure 1 The present invention provides an embodiment of an online optimization method for operating efficiency of a multi-track slope gravity energy storage system, comprising:

[0100] S1: According to the grid charging instruction P AGC_cha The slope gravity energy storage system operates in the energy storage condition. According to the charging efficiency η as claimed in claim 1 chaand the constraints under the energy storage condition as claimed in claim 2, the objective function is defined as:

[0101]

[0102] Where λ is the penalty factor;

[0103] The charging efficiency η of the slope type gravity energy storage system under the energy storage condition cha , specifically:

[0104]

[0105] Among them, η slope_cha_i is the mass moving efficiency of the i-th slope track under energy storage conditions, η chain_cha_i is the gear chain transmission efficiency of the i-th slope track under energy storage conditions, η gear_cha_i is the gearbox transmission efficiency of the i-th slope track under energy storage conditions, η G_cha_i is the traction motor efficiency of the i-th slope track under energy storage conditions, P G_cha_i is the input power of the traction motor of the i-th slope track under the energy storage condition;

[0106] The mass moving efficiency η of the i-th slope track under the energy storage condition slope_cha_i , whose expression is:

[0107]

[0108] Among them, θ is the inclination angle of the ramp track relative to the horizontal plane, and μ is the dynamic friction coefficient of the ramp track;

[0109] The gear chain transmission efficiency η of the i-th slope track under the energy storage condition chain_cha_i , whose expression is:

[0110]

[0111] Among them, n slope_i is the number of mass blocks on the i-th ramp track, m is the mass of a single mass block, g is the acceleration of gravity, ω is the angular velocity of the traction motor, r is the sprocket radius of the ramp track, k i is the gearbox ratio of the traction motor of the i-th slope track, P chain_loss Fixed losses for gear chain friction;

[0112] The gearbox transmission efficiency η of the i-th slope track under the energy storage condition gear_cha_i , whose expression is:

[0113]

[0114] Among them, Tidle is the no-load torque of the gearbox, K is a constant;

[0115] The traction motor efficiency η of the i-th slope track under the energy storage condition G_cha_i , whose expression is:

[0116]

[0117] Among them, I i is the input current of the traction motor of the i-th slope track, R s is the stator winding resistance, I f_i is the excitation current of the traction motor on the i-th slope track, R f is the rotor winding resistance, P fe is the iron loss of the traction motor, is the mechanical loss of the traction motor;

[0118] The constraints of the slope type gravity energy storage system under the energy storage condition include:

[0119] The traction motor input power P of the i-th slope track under the energy storage condition G _ cha _i satisfies:

[0120]

[0121] in,

[0122] The traction motor gearbox ratio k of the i-th slope track i satisfy:

[0123] k i ∈k n

[0124] Among them, k n Optional ratio for gearbox;

[0125] The number of mass blocks n on the i-th ramp track slope_i satisfy:

[0126] n slope_i ∈[0,n slope,max ](n slope_i ∈Z)

[0127] Among them, n slope，max is the maximum number of mass blocks on the ramp track.

[0128] According to the grid charging instruction P AGC_dis The slope type gravity energy storage system operates in the energy release condition. According to the discharge efficiency η as claimed in claim 1 disand the constraints under the energy release condition as claimed in claim 3, the objective function is defined as:

[0129]

[0130] The discharge efficiency η of the slope type gravity energy storage system under the energy release condition dis , specifically:

[0131]

[0132] Among them, η slope_dis_i is the mass moving efficiency of the i-th slope track under the energy release condition, η chain_dis_i is the gear chain transmission efficiency of the i-th slope track under the energy release condition, η gear_dis_i is the gearbox transmission efficiency of the i-th slope track under the energy release condition, η G_dis_i is the traction motor efficiency of the i-th slope track under the energy release condition, P G_dis_i is the output power of the traction motor of the i-th slope track under the energy release condition;

[0133] The mass moving efficiency η of the i-th slope track under the energy release condition slope_dis_i , whose expression is:

[0134]

[0135] Among them, θ is the inclination angle of the ramp track relative to the horizontal plane, and μ is the dynamic friction coefficient of the ramp track;

[0136] The gear chain transmission efficiency η of the i-th slope track under the energy release condition chain_dis_i , whose expression is:

[0137]

[0138] Among them, n slope_i is the number of mass blocks on the i-th ramp track, m is the mass of a single mass block, g is the acceleration of gravity, ω is the angular velocity of the traction motor, r is the sprocket radius of the ramp track, k i is the gearbox ratio of the traction motor of the i-th slope track, P chain_loss Fixed losses for gear chain friction;

[0139] The gearbox transmission efficiency η of the i-th slope track under the energy release condition gear_dis_i , whose expression is:

[0140]

[0141] Among them, T idle is the no-load torque of the gearbox, K is a constant;

[0142] The traction motor efficiency η of the i-th slope track under the energy release condition G_dis_i , whose expression is:

[0143]

[0144] Among them, I i is the input current of the traction motor of the i-th slope track, R s is the stator winding resistance, I f_i is the excitation current of the traction motor on the i-th slope track, R f is the rotor winding resistance, P fe is the iron loss of the traction motor, Mechanical loss of traction motor.

[0145] The constraints of the slope type gravity energy storage system under the energy release condition include:

[0146] The output power P of the traction motor of the i-th slope track under the energy release condition is G_dis_i satisfy:

[0147]

[0148] in,

[0149] The traction motor gearbox ratio k of the i-th slope track i satisfy:

[0150] k i ∈k n

[0151] Among them, k n Optional ratio for gearbox;

[0152] The number of mass blocks n on the i-th ramp track slope_i satisfy:

[0153] n slope_i ∈[0,n slope，max ](n slope_i ∈Z)

[0154] Among them, n slope，max is the maximum number of mass blocks on the ramp track.

[0155] S2: Initialize variables:

[0156]

[0157] Initialize particle velocity:

[0158] v i =(v1, ..., v 2nG) T

[0159] S3: Calculate the fitness of each particle:

[0160]

[0161] Determine the global optimum and the individual optimum for each particle;

[0162] S4: Update particle velocity:

[0163] v i (t+1)=ω·v i (t)+c1·ss(x i , p i )+c2·ss(x i , g i )

[0164] Among them, ω is the inertia weight, c1 and c2 are learning factors, and p i is the optimal position of individual particles, g i is the global optimal position of the particle, and the function ss(x, y) is the exchange function;

[0165] S5: Update particle position:

[0166] x i (t+1)=apply_operations(x i (t), v i (t+1))

[0167] S6: Determine whether each particle meets the constraint conditions, specifically:

[0168] Determine k i Whether it is within the optional ratio range;

[0169] Judgement slope_i Whether it is within the maximum number of masses allowed to move simultaneously on the ramp track;

[0170] judge Is it in the range of [0, 1]?

[0171] If there are particles that do not meet the constraint conditions, return to step S4;

[0172] S7: Calculate the new position fitness of each new particle:

[0173]

[0174] S8: Update individual optimality:

[0175] like

[0176] f(xi T )>f(p i T )

[0177] but

[0178] p i T =x i T

[0179] Update the global optimum:

[0180] like

[0181] f(x i T )>f(g i T )

[0182] but

[0183] g i T =x i T

[0184] S9: Repeat S4-S8 until the maximum number of iterations is met, and then stop iterating;

[0185] S10: Return the optimal control variables to the ramp-type gravity energy storage monitoring and energy management system to execute the latest control strategy.

[0186] In summary, the present invention is based on the power grid charging instruction P AGC_cha and discharge command P AGC_dis , the discrete particle swarm algorithm is used to calculate the overall charging efficiency η of the gravity energy storage system cha and discharge efficiency η dis The control variables to be maximized include the gearbox ratio k of the traction motor on each slope track i (i=1,2,...,n G , n G ∈Z) and the number of mass blocks on each ramp track n slope_i (i=1,2,...,n G , n G ∈Z), which effectively overcomes the problem that the existing control strategy fails to improve the power efficiency by optimizing the number of slope track mass blocks and the gearbox ratio of the traction motor, thereby maximizing the power efficiency of the entire system while ensuring that the grid dispatching instructions are met, and realizing online optimization of the system operation efficiency.

Claims

1. A method for online optimization of the operating efficiency of a multi-track slope gravity energy storage system, characterized in that: The slope gravity energy storage monitoring and energy management system is based on the grid charging instruction P AGC_cha and discharge command P AGC_dis , the discrete particle swarm algorithm is used to calculate the overall charging efficiency η of the gravity energy storage system cha and discharge efficiency η dis The maximized control variables include the gearbox ratio of the traction motor on each slope track and the number of mass blocks on each ramp track n slope =[n slope_1 ,n slope_2 ,…,η slope_i ,…,n slope_nG ], where i represents the ramp track number, n G is the number of ramp tracks, used for online control of multi-track ramp gravity energy storage system; The charging efficiency η of the slope type gravity energy storage system under the energy storage condition cha , specifically: Among them, η slope_cha_i is the mass moving efficiency of the i-th slope track under energy storage conditions, η chain_cha_i is the gear chain transmission efficiency of the i-th slope track under energy storage conditions, η gear_cha_i is the gearbox transmission efficiency of the i-th slope track under energy storage conditions, η G_cha_i is the traction motor efficiency of the i-th slope track under energy storage conditions, P G_cha_i is the input power of the traction motor of the i-th slope track under the energy storage condition; The mass moving efficiency η of the i-th slope track under the energy storage condition slope_cha_i , whose expression is: Among them, θ is the inclination angle of the ramp track relative to the horizontal plane, and μ is the dynamic friction coefficient of the ramp track; The gear chain transmission efficiency η of the i-th slope track under the energy storage condition chain_cha_i , whose expression is: Among them, n slope_i is the number of mass blocks on the i-th ramp track, m is the mass of a single mass block, g is the acceleration of gravity, ω is the angular velocity of the traction motor, r is the sprocket radius of the ramp track, k i is the gearbox ratio of the traction motor of the i-th slope track, P chain_loss Fixed losses for gear chain friction; The gearbox transmission efficiency η of the i-th slope track under the energy storage condition gear_cha_i , whose expression is: Among them, T idle is the no-load torque of the gearbox, K is a constant; The traction motor efficiency η of the i-th slope track under the energy storage condition G_cha_i , whose expression is: Among them, I i is the input current of the traction motor of the i-th slope track, R s is the stator winding resistance, I f_i is the excitation current of the traction motor on the i-th slope track, R f is the rotor winding resistance, P fe is the iron loss of the traction motor, is the mechanical loss of the traction motor; The discharge efficiency η of the slope type gravity energy storage system under the energy release condition dis , specifically: Among them, η slope_dis_i is the mass moving efficiency of the i-th slope track under the energy release condition, η chain_dis_i is the gear chain transmission efficiency of the i-th slope track under the energy release condition, η gear_dis_i is the gearbox transmission efficiency of the i-th slope track under the energy release condition, η G_dis_i is the traction motor efficiency of the i-th slope track under the energy release condition, P G_dis_i is the output power of the traction motor of the i-th slope track under the energy release condition; The mass moving efficiency η of the i-th slope track under the energy release condition slope_dis_i , whose expression is: Among them, θ is the inclination angle of the ramp track relative to the horizontal plane, and μ is the dynamic friction coefficient of the ramp track; The gear chain transmission efficiency η of the i-th slope track under the energy release condition chain_dis_i , whose expression is: Among them, n slope_i is the number of mass blocks on the i-th ramp track, m is the mass of a single mass block, g is the acceleration of gravity, ω is the angular velocity of the traction motor, r is the sprocket radius of the ramp track, k i is the gearbox ratio of the traction motor of the i-th slope track, P chain_loss Fixed losses for gear chain friction; The gearbox transmission efficiency η of the i-th slope track under the energy release condition gear_dis_i , whose expression is: Among them, T idle is the no-load torque of the gearbox, K is a constant; The traction motor efficiency η of the i-th slope track under the energy release condition G_dis_i , whose expression is: Among them, I i is the input current of the traction motor of the i-th slope track, R s is the stator winding resistance, I f_i is the excitation current of the traction motor on the i-th slope track, R f is the rotor winding resistance, P fe is the iron loss of the traction motor, Mechanical loss of traction motor.

2. The method for online optimization of operating efficiency of a multi-track slope gravity energy storage system according to claim 1, characterized in that: The constraints of the slope type gravity energy storage system under the energy storage condition include: The traction motor input power P of the i-th slope track under the energy storage condition G_cha_i satisfy: in, The traction motor gearbox ratio k of the i-th slope track i satisfy: k i ∈k n Among them, k n Optional ratio for gearbox; The number of mass blocks n on the i-th ramp track slope_i satisfy: n slope_i ∈[0,n slope,max ](n slope_i ∈Z) Among them, n slope,max is the maximum number of mass blocks on the ramp track.

3. The method for online optimization of operating efficiency of a multi-track slope gravity energy storage system according to claim 1, characterized in that: The constraints of the slope type gravity energy storage system under the energy release condition include: The output power P of the traction motor of the i-th slope track under the energy release condition is G_dis_i satisfy: in, The traction motor gearbox ratio k of the i-th slope track i satisfy: k i ∈k n Among them, k n Optional ratio for gearbox; The number of mass blocks n on the i-th ramp track slope_i satisfy: n slope_i ∈[0,n slope,max ](n slope_i ∈Z) Among them, n slope,max is the maximum number of mass blocks on the ramp track.

4. The method for online optimization of operating efficiency of a multi-track slope gravity energy storage system according to claim 1, characterized in that: The steps of calculating the control variables by the discrete particle swarm algorithm are: S401: According to the power grid charging instruction P AGC_cha The slope gravity energy storage system operates in the energy storage condition. According to the charging efficiency η as claimed in claim 1 cha and the constraints under the energy storage condition as claimed in claim 2, the objective function is defined as: Where λ is the penalty factor; According to the grid charging instruction P AGC_dis The slope type gravity energy storage system operates in the energy release condition. According to the discharge efficiency η as claimed in claim 1 dis and the constraints under the energy release condition as claimed in claim 3, the objective function is defined as: S402: Initialize variables: Initialize particle velocity: v i =(v1,…,v 2nG ) T S403: Calculate the fitness of each particle: Determine the global optimum and the individual optimum for each particle; S404: Update particle velocity: v i (t+1)=ω·v i (t)+c1·ss(x i ,p i )+c2·ss(x i ,g i ) Among them, ω is the inertia weight, c1 and c2 are learning factors, and p i is the optimal position of individual particles, g i is the global optimal position of the particle, and the function ss(x,y) is the exchange function; S405: Update particle position: x i (t+1)=apply_operations(x i (t),v i (t)+1)) S406: Determine whether each particle meets the constraint conditions, specifically: Determine k i Whether it is within the optional ratio range; Judgement slope_i Whether it is within the maximum number of masses allowed to move simultaneously on the ramp track; judge Is it in the range [0,1]? If there are particles that do not meet the constraint conditions, return to step S404; S407: Calculate the new position fitness of each new particle: S408: Update individual optimality: like f(x i T )>f(p i T ) but p i T =x i T Update the global optimum: like f(x i T )>f(g i T ) but g i T =x i T S409: Repeat S404 to S408 until the maximum number of iterations is met, and then stop iterating; S4010: Return the optimal control variables to the ramp-type gravity energy storage monitoring and energy management system to execute the latest control strategy.

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