Hybrid energy storage system double-layer capacity optimization configuration method for gravity energy storage and battery energy storage

Abstract

The invention provides a gravity energy storage and battery energy storage-oriented hybrid energy storage system double-layer capacity optimization configuration method, which comprises the following steps of: establishing a double-layer capacity optimization configuration model, performing capacity configuration optimization on an upper-layer model, and performing power scheduling optimization on a lower-layer model; initializing particle swarm parameters including particle quantity, inertia weight, maximum iteration times, speed limitation and the like, and randomly generating capacity configuration candidate particle swarms; for each particle, calling a lower-layer scheduling model to calculate the total system operation cost corresponding to the particle; evaluating the fitness value of each particle according to an upper objective function, and updating individual optimal and global optimal positions; adjusting the positions and the speeds of the particles according to an optimization rule; after iteration reaches a set number of times, outputting a gravity energy storage and battery energy storage capacity configuration scheme corresponding to the global optimal particle; the power grid frequency modulation instruction response precision of the hybrid energy storage system is effectively improved, and the construction and operation cost of the hybrid energy storage system is reduced to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the field of gravity energy storage technology, and in particular to a double-layer capacity optimization configuration method for a hybrid energy storage system of gravity energy storage and battery energy storage. Background Art

[0002] Multi-track slope gravity energy storage has the advantages of high safety and low cost, while battery energy storage has the advantages of fast response speed and high adjustment accuracy. Both types of energy storage have broad application prospects in the energy storage field.

[0003] Currently, the combined use of gravity energy storage and battery energy storage in grid frequency regulation can effectively improve the frequency regulation accuracy of gravity energy storage. However, the construction and operation costs of the hybrid energy storage system are bound to be higher than those of the gravity energy storage system alone. Therefore, it is necessary to optimize the capacity of multi-track gravity energy storage and battery energy storage to improve economic efficiency while ensuring frequency regulation accuracy. However, there is currently no double-layer capacity optimization configuration method for hybrid energy storage systems that combine gravity energy storage and battery energy storage. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a double-layer capacity optimization configuration method for a hybrid energy storage system of gravity energy storage and battery energy storage.

[0005] The technical solution of the present invention is: a double-layer capacity optimization configuration method for a hybrid energy storage system of gravity energy storage and battery energy storage, comprising the following steps:

[0006] S101) Establish an upper-level model for the double-layer capacity optimization configuration of a hybrid energy storage system for gravity energy storage and battery energy storage. The specific objective function expression is as follows:

[0007] J(C g ,C b )=a g C g +a b C b +F(p g ,p b ) (1)

[0008] Among them, a g and a b They represent the capacity unit price of multi-track gravity energy storage and the capacity unit price of battery energy storage, respectively. g and C b They represent the capacity of multi-track gravity energy storage and battery energy storage respectively, F(p g ,p b ) represents the total operating cost of gravity energy storage and battery energy storage within the dispatch period T;

[0009] The constraints of the upper-layer capacity optimization configuration model are as follows:

[0010] The capacity constraint expressions of the multi-track gravity energy storage system and the battery energy storage system are:

[0011] C g,min ≤C g ≤C g,max (2)

[0012] C b,min ≤C b ≤C b,max (3)

[0013] Among them, C b,max and C g,max are the upper limits of the capacity of battery energy storage and gravity energy storage, C b,min and C g,min are the lower limits of the capacity of battery energy storage and gravity energy storage respectively;

[0014] S102), initialize the capacity optimization configuration particle swarm parameters, including the number of particles M, inertia weight w0, maximum number of iterations y0, individual particle optimal position, global optimal position, particle speed limit conditions, and randomly generate capacity configuration candidate particle swarms under the constraints. Each particle in the particle swarm represents a set of capacity configurations for gravity energy storage and battery energy storage, expressed as (C g ,C b );

[0015] S103), configure candidate particles for each current capacity (C g ,C b ), calling the lower-layer power optimization scheduling algorithm to calculate the total operating cost of the system under the capacity configuration within the scheduling period T;

[0016] S104), calculating the fitness value of each capacity configuration candidate particle according to the objective function of the upper capacity optimization configuration model described in step S101;

[0017] S105), after calculating the fitness values ​​of all particles, determine whether the fitness value of each capacity configuration candidate particle is better than the fitness value of the particle's historical optimal position. If so, update the particle position to the particle's historical optimal position; if not, do not update the particle's historical optimal position.

[0018] S106), sequentially determining whether the fitness value of the historical optimal position of each capacity configuration candidate particle is better than the fitness value of the global optimal position, if so, updating the historical optimal position of the particle to the global optimal position, if not, not updating the global optimal position;

[0019] S107) Update the speed of each capacity configuration optimization particle based on the individual historical optimal position and the global optimal position. The particle speed update formula is as follows:

[0020]

[0021] Among them, w0 is the inertia weight, y is the current iteration number, is the velocity of the kth particle in the next iteration cycle, is the speed of the kth particle in the current iteration cycle, is the position of the kth particle in the current iteration cycle, c1 and c2 are the individual learning factor and the group learning factor respectively, r1 and r2 are random numbers between [0,1], pBest k is the historical optimal position of the kth particle, gBest is the global optimal position;

[0022] S108) After the capacity optimization configuration particle is updated, it is determined whether the speed of the two variables of gravity energy storage capacity and battery energy storage capacity in the particle exceeds the set speed limit [V min ,V max ], if it exceeds the upper limit, the speed of the variable is adjusted to V max If it exceeds the lower limit, the speed of the variable is adjusted to V min , if the speed limit is not exceeded, the speed of the variable is not adjusted;

[0023] S109) Update the position of each capacity configuration optimization particle based on the individual historical optimal position and the global optimal position. The particle position update formula is as follows:

[0024]

[0025] in, is the position of the kth particle in the next iteration cycle.

[0026] S110), determine each capacity optimization configuration particle (C g ,C b ) Whether the updated position exceeds the capacity constraint, if the gravity energy storage capacity C g Exceeds upper limit C g,max Then let the gravitational energy storage capacity C of the particle be g =C g,max If the lower limit C is exceeded g,min Then let the gravitational energy storage capacity C of the particle be g =C g,min If the gravity energy storage capacity constraint is not exceeded, C is not adjusted. g Similarly, if the battery storage capacity C b Exceeds upper limit C b,maxThen let the battery energy storage capacity C of the particle be b =C b,max If the lower limit C is exceeded b,min Then let the battery energy storage capacity C of the particle be b =C b,min If the battery storage capacity constraint is not exceeded, C is not adjusted. b ;

[0027] S111), determine whether the number of iterations has reached the maximum number of iterations, and if so, output the gravity energy storage capacity C corresponding to the optimal particle in the capacity optimization configuration particles. g,best and battery energy storage capacity C b,best If it is not reached, set y=y+1 and execute step S103.

[0028] Preferably, in step S103, the lower-layer power optimization scheduling algorithm is called to calculate the total operating cost of the system under the capacity configuration within the scheduling period T, and the specific steps are as follows:

[0029] S201) Establish a lower-layer power optimization scheduling model for a double-layer capacity optimization configuration method of a hybrid energy storage system for gravity energy storage and battery energy storage:

[0030] The objective function expression of the lower-level power optimization scheduling model is as follows:

[0031] F(p g ,p b )=α|P net -p g -p b |+β b L b (p b ) (6)

[0032] Among them, P net is the power dispatch instruction of the power grid, p g and p b are the power of gravity energy storage and battery energy storage respectively, α is the penalty cost coefficient of unit deviation power, β b is the battery operation cost weight coefficient, L b (p b ) is the battery operating cost, and its expressions are:

[0033]

[0034] Where S is the battery cycle life, C b is the battery energy storage capacity, p b is the power of battery storage, Δt is the duration of the cycle;

[0035] The constraints of the multi-track gravity energy storage and battery energy storage in the lower-level operation optimization scheduling model are as follows:

[0036] The power constraint expression of the multi-track gravity energy storage system is:

[0037] P0n min ≤p g ≤P0n max (8)

[0038] Among them, n min and n max are the lower and upper limits of the number of mass blocks allowed on the slope of the multi-track gravity energy storage system, P0 is the power generated by a single mass block, and p g is the power of gravity energy storage, specifically:

[0039] p g =nP0 (9)

[0040] Where n is the number of mass blocks on the slope;

[0041] The capacity constraint expression of the multi-track gravity energy storage system is:

[0042] 0≤E g ≤C g (10)

[0043] Among them, C g is the capacity of gravity energy storage, E g is the remaining power of gravity energy storage, specifically:

[0044] E g =E g0 -p g0 Δt (11)

[0045] Among them, E g0 is the remaining amount of gravity energy storage in the previous cycle, Δt is the duration of the cycle, p g0 The power of gravity energy storage in the previous cycle;

[0046] The power constraint expression of battery energy storage is:

[0047] -p b,max ≤p b ≤p b,max (12)

[0048] Among them, p b,max is the rated power of the battery energy storage, p b The power of energy stored in the battery;

[0049] The SOC constraint expression of battery energy storage is:

[0050] SOC min ≤SOC≤SOC max (13)

[0051] Among them, SOC min and SOC max They are the lower and upper limits of the battery energy storage SOC, respectively. SOC is the state of charge of the battery energy storage, specifically:

[0052]

[0053] Among them, SOC0 is the state of charge of the battery energy storage cycle, Δt is the cycle duration, p b0 The power of the battery stored in the previous cycle;

[0054] The SOC regression constraint expression of battery energy storage is:

[0055] SOC b,0 =SOC b,T (15)

[0056] SOC b,0 and SOC b,T They are the initial SOC of the battery energy storage before dispatch and the SOC after the entire dispatch cycle;

[0057] S202), read the typical daily load curve and the capacity given by the upper layer (C g ,C b ), set the total number of scheduling cycles q0, set the scheduling cycle number i = 1, and initialize the power optimization scheduling particle swarm parameters, including the number of particles N, inertia weight w1, maximum number of iterations m1, individual particle optimal position, global optimal position, and particle speed limit;

[0058] S203) Determine whether i is less than q0 at this time. If it is less than q0, it indicates that the calculation of the operating cost of the entire scheduling cycle has not been completed, and step S204 is executed. If it is greater than q0, it indicates that the calculation of the operating cost of the entire scheduling cycle has been completed, and step S215 is executed;

[0059] S204) In the i-th time period, the maximum and minimum values ​​of the number of mass blocks that can be adjusted in the multi-track gravity energy storage system in the current cycle are detected, and the upper limit is set to n max , the lower limit is n min ;

[0060] S205) Randomly generate a power optimization scheduling candidate particle group under the constraints of step S201, and each particle represents the total number n of mass blocks on all slope tracks and the battery power p in the time period. b A set of configurations, namely (n,p b );

[0061] S206), according to the objective function of the lower model in step S201, that is, formula (6), the fitness value of each power optimization scheduling particle is solved;

[0062] S207), after calculating the fitness values ​​of all particles, determine whether the fitness value of each power optimization scheduling particle is better than the fitness value of the individual historical optimal position of the particle. If it is better, the position of the particle is updated to the individual historical optimal position of the particle; if it is not better, the individual historical optimal position of the particle is not updated;

[0063] S208), determine in turn whether the fitness value of the historical optimal position of each power optimization scheduling particle is better than the fitness value of the global optimal position, if better, update the historical optimal position of the particle to the global optimal position, if not better, do not update the global optimal position;

[0064] S209) Update the speed of each particle based on the individual historical optimal position and the global optimal position. The particle speed update formula is as follows:

[0065]

[0066] Among them, w1 is the inertia weight, m ​​is the current number of iterations, is the velocity of the jth particle in the next iteration cycle, is the speed of the jth particle in the current iteration cycle, is the position of the jth particle in the current iteration cycle, c3 and c4 are the individual learning factor and the group learning factor respectively, r3 and r4 are random numbers between [0,1], pBest j is the historical optimal position of the jth particle, and gBest1 is the global optimal position.

[0067] S210) After the power optimization scheduling particle is updated, it is determined whether the speed of the two variables, the number of mass blocks in the particle and the battery energy storage power, exceeds the set speed limit. If it exceeds the upper limit, the speed of the variable is adjusted to If it exceeds the lower limit, the speed of the variable is adjusted to If the speed limit is not exceeded, the speed of the variable is not adjusted;

[0068] S211) Update the position of each particle according to the individual historical optimal position and the global optimal position. The particle position update formula is as follows:

[0069]

[0070] in, is the position of the jth particle in the next iteration cycle.

[0071] S212), determine each power optimization scheduling particle (n, p b ) Whether the updated position exceeds the set constraints, if the number of mass blocks n exceeds the upper limit of the number of mass blocks n max Then let the number of mass blocks in the particle n = n max , if the number of mass blocks exceeds the lower limit n min Then let the number of mass blocks in the particle n = n min , if n does not exceed the mass block constraint condition, it will not change. Similarly, if the battery energy storage power p b Exceeded the battery power limit p b,max Then let the battery energy storage power p in the particle be b =p b,max If the battery power limit is exceeded -p b,max Then let the battery energy storage power p in the particle be b =-p b,max , if p b If the battery power constraint is not exceeded, no change will occur;

[0072] S213), determine whether the number of iterations has reached the maximum number of iterations at this time, if it has reached it, execute step S213, if not, set m=m+1, and execute step S206;

[0073] S214), output the optimal power of gravity energy storage and battery energy storage and calculate the operating cost within the cycle, update the capacity of gravity energy storage and battery energy storage at this time, and set i=i+1, and execute step S203;

[0074] S215) Calculate the operating cost within the scheduling period T and return it to the upper layer capacity optimization configuration algorithm.

[0075] Preferably, in step S203, the maximum number of mass blocks and the minimum number of mass blocks that can be adjusted in the multi-track gravity energy storage system in the current cycle are detected, and the specific steps are:

[0076] S301) Read the total number of mass blocks n1 on all tracks in the previous cycle, initialize the track number h, and set n max =n min =n1;

[0077] S302), detecting whether the crane on the hth track can perform the operation of adding or removing the mass block, if yes, executing step S303, if not, executing step S304;

[0078] S303) Detect whether there is a mass block at the end of the current running direction of the hth track. If so, the upper limit of the number of mass blocks on the slope track is n. max Unchanged, lower limit nmin Subtract 1; if there is no mass block, set the upper limit of the number of mass blocks to n max Add 1, lower limit n min constant;

[0079] S304) Detect whether there is a mass block at the starting end of the current motion direction of the hth track. If so, the upper limit of the number of mass blocks n max Minus 1, if it does not exist, the upper limit of the number of mass blocks n max constant;

[0080] S305) Determine whether all tracks have been detected, and if so, output the upper limit n of the number of mass blocks. max and lower limit n min Otherwise, let h=h+1 and re-execute steps S302, S303, and S304.

[0081] The beneficial effects of the present invention are:

[0082] 1. The present invention proposes a two-layer capacity optimization configuration method for a hybrid energy storage system of gravity energy storage and battery energy storage, establishes an upper-layer capacity optimization configuration model and a lower-layer power optimization scheduling model, effectively improves the accuracy of the hybrid energy storage system in responding to the grid frequency regulation instructions, and minimizes the construction and operation costs of the hybrid energy storage system while ensuring the accuracy and speed of the hybrid energy storage system in responding to the grid frequency regulation instructions, thereby achieving the optimal balance between the frequency regulation performance and economy of the hybrid energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 A schematic diagram of the capacity optimization process of the present invention;

[0084] Figure 2 A flow chart of the upper layer capacity configuration optimization algorithm of the present invention;

[0085] Figure 3 Schematic diagram of the flow of the lower layer power optimization scheduling algorithm of the present invention. DETAILED DESCRIPTION

[0086] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0087] like Figure 1 and Figure 2 As shown, this embodiment provides a dual-layer capacity optimization configuration method for a hybrid energy storage system of gravity energy storage and battery energy storage, including the following steps:

[0088] S101) Establish an upper-level model for the double-layer capacity optimization configuration of a hybrid energy storage system for gravity energy storage and battery energy storage. The specific objective function expression is as follows:

[0089] J(Cg ,C b )=a g C g +a b C b +F(p g ,p b ) (1)

[0090] Among them, a g and a b They represent the capacity unit price of multi-track gravity energy storage and the capacity unit price of battery energy storage, respectively. g and C b They represent the capacity of multi-track gravity energy storage and battery energy storage respectively, F(p g ,p b ) represents the total operating cost of gravity energy storage and battery energy storage within the scheduling period T.

[0091] The constraints of the upper-layer capacity optimization configuration model are as follows:

[0092] The capacity constraint expressions of the multi-track gravity energy storage system and the battery energy storage system are:

[0093] C g,min ≤C g ≤C g,max (2)

[0094] C b,min ≤C b ≤C b,max (3)

[0095] Among them, C b,max and C g,max are the upper limits of the capacity of battery energy storage and gravity energy storage, C b,min and C g,min are the lower limits of the capacity of battery energy storage and gravity energy storage respectively;

[0096] S102), initialize the capacity optimization configuration particle swarm parameters, including the number of particles M, inertia weight w0, maximum number of iterations y0, individual particle optimal position, global optimal position, particle speed limit conditions, and randomly generate capacity configuration candidate particle swarms under the constraints. Each particle in the particle swarm represents a set of capacity configurations for gravity energy storage and battery energy storage, expressed as (C g ,C b );

[0097] S103), configure candidate particles for each current capacity (C g ,C b ), calling the lower-layer power optimization scheduling algorithm to calculate the total operating cost of the system under the capacity configuration within the scheduling period T;

[0098] S104), calculating the fitness value of each capacity configuration candidate particle according to the objective function of the upper capacity optimization configuration model described in step S101;

[0099] S105), after calculating the fitness values ​​of all particles, determine whether the fitness value of each capacity configuration candidate particle is better than the fitness value of the particle's historical optimal position. If so, update the particle position to the particle's historical optimal position; if not, do not update the particle's historical optimal position.

[0100] S106), sequentially determining whether the fitness value of the historical optimal position of each capacity configuration candidate particle is better than the fitness value of the global optimal position, if so, updating the historical optimal position of the particle to the global optimal position, if not, not updating the global optimal position;

[0101] S107) Update the speed of each capacity configuration optimization particle based on the individual historical optimal position and the global optimal position. The particle speed update formula is as follows:

[0102]

[0103] Among them, w0 is the inertia weight, y is the current iteration number, is the velocity of the kth particle in the next iteration cycle, is the speed of the kth particle in the current iteration cycle, is the position of the kth particle in the current iteration cycle, c1 and c2 are the individual learning factor and the group learning factor respectively, r1 and r2 are random numbers between [0,1], pBest k is the historical optimal position of the kth particle, gBest is the global optimal position;

[0104] S108) After the capacity optimization configuration particle is updated, it is determined whether the speed of the two variables of gravity energy storage capacity and battery energy storage capacity in the particle exceeds the set speed limit [V min ,V max ], if it exceeds the upper limit, the speed of the variable is adjusted to V max If it exceeds the lower limit, the speed of the variable is adjusted to V min , if the speed limit is not exceeded, the speed of the variable is not adjusted;

[0105] S109) Update the position of each capacity configuration optimization particle based on the individual historical optimal position and the global optimal position. The particle position update formula is as follows:

[0106]

[0107] in, is the position of the kth particle in the next iteration cycle;

[0108] S110), determine each capacity optimization configuration particle (C g ,C b ) Whether the updated position exceeds the capacity constraint, if the gravity energy storage capacity C g Exceeds upper limit C g,max Then let the gravitational energy storage capacity C of the particle be g =C g,max If the lower limit C is exceeded g,min Then let the gravitational energy storage capacity C of the particle be g =C g,min If the gravity energy storage capacity constraint is not exceeded, C is not adjusted. g Similarly, if the battery storage capacity C b Exceeds upper limit C b,max Then let the battery energy storage capacity C of the particle be b =C b,max If the lower limit C is exceeded b,min Then let the battery energy storage capacity C of the particle be b =C b,min If the battery storage capacity constraint is not exceeded, C is not adjusted. b ;

[0109] S111), determine whether the number of iterations has reached the maximum number of iterations, and if so, output the gravity energy storage capacity C corresponding to the optimal particle in the capacity optimization configuration particles. g,best and battery energy storage capacity C b,best If it is not reached, set y=y+1 and execute step S103.

[0110] like Figure 3 As shown, this embodiment provides a lower-layer power optimization scheduling algorithm for a hybrid energy storage system of gravity energy storage and battery energy storage, including the following steps:

[0111] As a preferred embodiment of this invention, in step S103, the lower-layer power optimization scheduling algorithm is called to calculate the total operating cost of the system under the capacity configuration within the scheduling period T. The specific steps are as follows:

[0112] S201) Establish a lower-layer power optimization scheduling model for a double-layer capacity optimization configuration method of a hybrid energy storage system for gravity energy storage and battery energy storage:

[0113] The objective function expression of the lower-level power optimization scheduling model is as follows:

[0114] F(p g ,p b )=α|P net -p g -pb |+β b L b (p b ) (6)

[0115] Among them, P net is the power dispatch instruction of the power grid, p g and p b are the power of gravity energy storage and battery energy storage respectively, α is the penalty cost coefficient of unit deviation power, β b is the battery operation cost weight coefficient, L b (p b ) is the battery operating cost, and its expressions are:

[0116]

[0117] Where S is the battery cycle life, C b is the battery energy storage capacity, p b is the power of battery storage, Δt is the duration of the cycle;

[0118] The constraints of the multi-track gravity energy storage and battery energy storage in the lower-level operation optimization scheduling model are as follows:

[0119] The power constraint expression of the multi-track gravity energy storage system is:

[0120] P0n min ≤p g ≤P0n max (8)

[0121] Among them, n min and n max are the lower and upper limits of the number of mass blocks allowed on the slope of the multi-track gravity energy storage system, P0 is the power generated by a single mass block, and p g is the power of gravity energy storage, specifically:

[0122] p g =nP0 (9)

[0123] Where n is the number of mass blocks on the slope;

[0124] The capacity constraint expression of the multi-track gravity energy storage system is:

[0125] 0≤E g ≤C g (10)

[0126] Among them, C g is the capacity of gravity energy storage, E g is the remaining power of gravity energy storage, specifically:

[0127] Eg =E g0 -p g0 Δt (11)

[0128] Among them, E g0 is the remaining amount of gravity energy storage in the previous cycle, Δt is the duration of the cycle, p g0 The power of gravity energy storage in the previous cycle;

[0129] The power constraint expression of battery energy storage is:

[0130] -p b,max ≤p b ≤p b,max (12)

[0131] Among them, p b,max is the rated power of the battery energy storage, p b The power of energy stored in the battery;

[0132] The SOC constraint expression of battery energy storage is:

[0133] SOC min ≤SOC≤SOC max (13)

[0134] Among them, SOC min and SOC max They are the lower and upper limits of the battery energy storage SOC, respectively. SOC is the state of charge of the battery energy storage, specifically:

[0135]

[0136] Among them, SOC0 is the state of charge of the battery energy storage cycle, Δt is the cycle duration, p b0 The power of the battery stored in the previous cycle;

[0137] The SOC regression constraint expression of battery energy storage is:

[0138] SOC b,0 =SOC b,T (15)

[0139] SOC b,0 and SOC b,T They are the initial SOC of the battery energy storage before dispatch and the SOC after the entire dispatch cycle;

[0140] S202), read the typical daily load curve and the capacity given by the upper layer (C g ,C b), set the total number of scheduling cycles q0, set the scheduling cycle number i = 1, and initialize the power optimization scheduling particle swarm parameters, including the number of particles N, inertia weight w1, maximum number of iterations m1, individual particle optimal position, global optimal position, and particle speed limit;

[0141] S203) Determine whether i is less than q0 at this time. If it is less than q0, it indicates that the calculation of the operating cost of the entire scheduling cycle has not been completed, and step S204 is executed. If it is greater than q0, it indicates that the calculation of the operating cost of the entire scheduling cycle has been completed, and step S215 is executed;

[0142] S204) In the i-th time period, the maximum and minimum values ​​of the number of mass blocks that can be adjusted in the multi-track gravity energy storage system in the current cycle are detected, and the upper limit is set to n max , the lower limit is n min ;

[0143] S205) Randomly generate a power optimization scheduling candidate particle group under the constraints of step S201, and each particle represents the total number n of mass blocks on all slope tracks and the battery power p in the time period. b A set of configurations, namely (n,p b );

[0144] S206), according to the objective function of the lower model in step S201, that is, formula (6), the fitness value of each power optimization scheduling particle is solved;

[0145] S207), after calculating the fitness values ​​of all particles, determine whether the fitness value of each power optimization scheduling particle is better than the fitness value of the individual historical optimal position of the particle. If it is better, the position of the particle is updated to the individual historical optimal position of the particle; if it is not better, the individual historical optimal position of the particle is not updated;

[0146] S208), determine in turn whether the fitness value of the historical optimal position of each power optimization scheduling particle is better than the fitness value of the global optimal position, if better, update the historical optimal position of the particle to the global optimal position, if not better, do not update the global optimal position;

[0147] S209) Update the speed of each particle based on the individual historical optimal position and the global optimal position. The particle speed update formula is as follows:

[0148]

[0149] Among them, w1 is the inertia weight, m ​​is the current number of iterations, is the velocity of the jth particle in the next iteration cycle, is the speed of the jth particle in the current iteration cycle, is the position of the jth particle in the current iteration cycle, c3 and c4 are the individual learning factor and the group learning factor respectively, r3 and r4 are random numbers between [0,1], pBest j is the historical optimal position of the jth particle, and gBest1 is the global optimal position;

[0150] S210) After the power optimization scheduling particle is updated, it is determined whether the speed of the two variables, the number of mass blocks in the particle and the battery energy storage power, exceeds the set speed limit. If it exceeds the upper limit, the speed of the variable is adjusted to If it exceeds the lower limit, the speed of the variable is adjusted to If the speed limit is not exceeded, the speed of the variable is not adjusted;

[0151] S211) Update the position of each particle according to the individual historical optimal position and the global optimal position. The particle position update formula is as follows:

[0152]

[0153] in, is the position of the jth particle in the next iteration cycle;

[0154] S212), determine each power optimization scheduling particle (n, p b ) Whether the updated position exceeds the set constraints, if the number of mass blocks n exceeds the upper limit of the number of mass blocks n max Then let the number of mass blocks in the particle n = n max , if the number of mass blocks exceeds the lower limit n min Then let the number of mass blocks in the particle n = n min , if n does not exceed the mass block constraint condition, it will not change. Similarly, if the battery energy storage power p b Exceeded the battery power limit p b,max Then let the battery energy storage power p in the particle be b =p b,max If the battery power limit is exceeded -p b,max Then let the battery energy storage power p in the particle be b =-p b,max , if p b If the battery power constraint is not exceeded, no change will occur;

[0155] S213), determine whether the number of iterations has reached the maximum number of iterations at this time, if it has reached it, execute step S213, if not, set m=m+1, and execute step S206;

[0156] S214), output the optimal power of gravity energy storage and battery energy storage and calculate the operating cost within the cycle, update the capacity of gravity energy storage and battery energy storage at this time, and set i=i+1, and execute step S203;

[0157] S215) Calculate the operating cost within the scheduling period T and return it to the upper layer capacity optimization configuration algorithm.

[0158] As a preferred embodiment of this invention, in step S203, the maximum number of mass blocks and the minimum number of mass blocks that can be adjusted in the multi-track gravity energy storage system in the current cycle are detected, and the specific steps are:

[0159] S301) Read the total number of mass blocks n1 on all tracks in the previous cycle, initialize the track number h, and set n max =n min =n1;

[0160] S302), detecting whether the crane on the hth track can perform the operation of adding or removing the mass block, if yes, executing step S303, if not, executing step S304;

[0161] S303) Detect whether there is a mass block at the end of the current running direction of the hth track. If so, the upper limit of the number of mass blocks on the slope track is n. max Unchanged, lower limit n min Subtract 1; if there is no mass block, set the upper limit of the number of mass blocks to n max Add 1, lower limit n min constant;

[0162] S304) Detect whether there is a mass block at the starting end of the current motion direction of the hth track. If so, the upper limit of the number of mass blocks n max Minus 1, if it does not exist, the upper limit of the number of mass blocks n max constant;

[0163] S305) Determine whether all tracks have been detected, and if so, output the upper limit n of the number of mass blocks. max and lower limit n min Otherwise, let h=h+1 and re-execute steps S302, S303, and S304.

[0164] The above embodiments and descriptions are only for explaining the principles and best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, which shall fall within the scope of the invention to be protected.

Claims

1. A double-layer capacity optimization configuration method for a hybrid energy storage system of gravity energy storage and battery energy storage, characterized in that: The two-layer capacity optimization configuration method includes an upper-layer capacity optimization configuration algorithm and a lower-layer power optimization scheduling algorithm. The specific steps are as follows: S101) Establish an upper-level model for the double-layer capacity optimization configuration of a hybrid energy storage system for gravity energy storage and battery energy storage. The specific objective function expression is as follows: J(C g ,C b )=a g C g +a b C b +F(p g ,p b ) (1) Among them, a g and a b They represent the capacity unit price of multi-track gravity energy storage and the capacity unit price of battery energy storage, respectively. g and C b They represent the capacity of multi-track gravity energy storage and battery energy storage respectively, F(p g ,p b ) represents the total operating cost of gravity energy storage and battery energy storage within the dispatch period T; The constraints of the upper-layer capacity optimization configuration model are as follows: The capacity constraint expressions of the multi-track gravity energy storage system and the battery energy storage system are: C g,min ≤C g ≤C g,max (2) C b,min ≤C b ≤C b,max (3) Among them, C b,max and C g,max are the upper limits of the capacity of battery energy storage and gravity energy storage, C b,min and C g,min are the lower limits of the capacity of battery energy storage and gravity energy storage respectively; S102), initialize the capacity optimization configuration particle swarm parameters, including the number of particles M, inertia weight w0, maximum number of iterations y0, individual particle optimal position, global optimal position, particle speed limit conditions, and randomly generate capacity configuration candidate particle swarms under the constraints. Each particle in the particle swarm represents a set of capacity configurations for gravity energy storage and battery energy storage, expressed as (C g ,C b ); S103), configure candidate particles for each current capacity (C g ,C b ), calling the lower-layer power optimization scheduling algorithm to calculate the total operating cost of the system under the capacity configuration within the scheduling period T; S104), calculating the fitness value of each capacity configuration candidate particle according to the objective function of the upper capacity optimization configuration model described in step S101; S105), after calculating the fitness values ​​of all particles, determine whether the fitness value of each capacity configuration candidate particle is better than the fitness value of the particle's historical optimal position. If so, update the particle position to the particle's historical optimal position; if not, do not update the particle's historical optimal position. S106), sequentially determining whether the fitness value of the historical optimal position of each capacity configuration candidate particle is better than the fitness value of the global optimal position, if so, updating the historical optimal position of the particle to the global optimal position, if not, not updating the global optimal position; S107) Update the speed of each capacity configuration optimization particle based on the individual historical optimal position and the global optimal position. The particle speed update formula is as follows: Among them, w0 is the inertia weight, y is the current iteration number, is the velocity of the kth particle in the next iteration cycle, is the speed of the kth particle in the current iteration cycle, is the position of the kth particle in the current iteration cycle, c1 and c2 are the individual learning factor and the group learning factor respectively, r1 and r2 are random numbers between [0,1], pBest k is the historical optimal position of the kth particle, gBest is the global optimal position; S108) After the capacity optimization configuration particle is updated, it is determined whether the speed of the two variables of gravity energy storage capacity and battery energy storage capacity in the particle exceeds the set speed limit [V min ,V max ], if it exceeds the upper limit, the speed of the variable is adjusted to V max If it exceeds the lower limit, the speed of the variable is adjusted to V min , if the speed limit is not exceeded, the speed of the variable is not adjusted; S109) Update the position of each capacity configuration optimization particle based on the individual historical optimal position and the global optimal position. The particle position update formula is as follows: in, is the position of the kth particle in the next iteration cycle; S110), determine each capacity optimization configuration particle (C g ,C b ) Whether the updated position exceeds the capacity constraint, if the gravity energy storage capacity C g Exceeds upper limit C g,max Then let the gravitational energy storage capacity C of the particle be g =C g,max If the lower limit C is exceeded g,min Then let the gravitational energy storage capacity C of the particle be g =C g,min If the gravity energy storage capacity constraint is not exceeded, C is not adjusted. g Similarly, if the battery storage capacity C b Exceeds upper limit C b,max Then let the battery energy storage capacity C of the particle be b =C b,max If the lower limit C is exceeded b,min Then let the battery energy storage capacity C of the particle be b =C b,min If the battery storage capacity constraint is not exceeded, C is not adjusted. b ; S111), determine whether the number of iterations has reached the maximum number of iterations, and if so, output the gravity energy storage capacity C corresponding to the optimal particle in the capacity optimization configuration particles. g,best and battery energy storage capacity C b,best If it is not reached, set y=y+1 and execute step S103.

2. The method for optimizing the dual-layer capacity of a hybrid energy storage system for gravity energy storage and battery energy storage according to claim 1, characterized in that: Step S103 calls the lower-layer power optimization scheduling algorithm to calculate the total operating cost of the system under the capacity configuration within the scheduling period T. The specific steps are: S201) Establish a lower-layer power optimization scheduling model for a double-layer capacity optimization configuration method of a hybrid energy storage system for gravity energy storage and battery energy storage: The objective function expression of the lower-level power optimization scheduling model is as follows: F(p g ,p b )=α|P net -p g -p b |+β b 50 b (p b ) (6) Among them, P net is the power dispatch instruction of the power grid, p g and p b are the power of gravity energy storage and battery energy storage respectively, α is the penalty cost coefficient of unit deviation power, β b is the battery operation cost weight coefficient, L b (p b ) is the battery operating cost, and its expressions are: Where S is the battery cycle life, C b is the battery energy storage capacity, p b is the power of battery storage, Δt is the duration of the cycle; The constraints of the multi-track gravity energy storage and battery energy storage in the lower-level operation optimization scheduling model are as follows: The power constraint expression of the multi-track gravity energy storage system is: P0n min ≤p g ≤P0n max (8) Among them, n min and n max are the lower and upper limits of the number of mass blocks allowed on the slope of the multi-track gravity energy storage system, P0 is the power generated by a single mass block, and p g is the power of gravity energy storage, specifically: p g =nP0 (9) Where n is the number of mass blocks on the slope; The capacity constraint expression of the multi-track gravity energy storage system is: 0≤E g ≤C g (10) Among them, C g is the capacity of gravity energy storage, E g is the remaining power of gravity energy storage, specifically: AND g =And g0 -p g0 Δt (11) Among them, E g0 is the remaining amount of gravity energy storage in the previous cycle, Δt is the duration of the cycle, p g0 The power of gravity energy storage in the previous cycle; The power constraint expression of battery energy storage is: -p b,max ≤p b ≤p b,max (12) Among them, p b,max is the rated power of the battery energy storage, p b The power of energy stored in the battery; The SOC constraint expression of battery energy storage is: SOC min ≤SOC≤SOC max (13) Among them, SOC min and SOC max They are the lower and upper limits of the battery energy storage SOC, respectively. SOC is the state of charge of the battery energy storage, specifically: Among them, SOC0 is the state of charge of the battery energy storage cycle, Δt is the cycle duration, p b0 The power of the battery stored in the previous cycle; The SOC regression constraint expression of battery energy storage is: SOCIETY b,0 =SOC b,T (15) SOC b,0 and SOC b,T They are the initial SOC of the battery energy storage before dispatch and the SOC after the entire dispatch cycle; S202), read the typical daily load curve and the capacity given by the upper layer (C g ,C b ), set the total number of scheduling cycles q0, set the scheduling cycle number i = 1, and initialize the power optimization scheduling particle swarm parameters, including the number of particles N, inertia weight w1, maximum number of iterations m1, individual particle optimal position, global optimal position, and particle speed limit; S203) Determine whether i is less than q0 at this time. If it is less than q0, it indicates that the calculation of the operating cost of the entire scheduling cycle has not been completed, and step S204 is executed. If it is greater than q0, it indicates that the calculation of the operating cost of the entire scheduling cycle has been completed, and step S215 is executed; S204) In the i-th time period, the maximum and minimum values ​​of the number of mass blocks that can be adjusted in the multi-track gravity energy storage system in the current cycle are detected, and the upper limit is set to n max , the lower limit is n min ; S205) Randomly generate a power optimization scheduling candidate particle group under the constraints of step S201, and each particle represents the total number n of mass blocks on all slope tracks and the battery power p in the time period. b A set of configurations, namely (n,p b ); S206), according to the objective function of the lower model in step S201, that is, formula (6), the fitness value of each power optimization scheduling particle is solved; S207), after calculating the fitness values ​​of all particles, determine whether the fitness value of each power optimization scheduling particle is better than the fitness value of the individual historical optimal position of the particle. If it is better, the position of the particle is updated to the individual historical optimal position of the particle; if it is not better, the individual historical optimal position of the particle is not updated; S208), determine in turn whether the fitness value of the historical optimal position of each power optimization scheduling particle is better than the fitness value of the global optimal position, if better, update the historical optimal position of the particle to the global optimal position, if not better, do not update the global optimal position; S209) Update the speed of each particle based on the individual historical optimal position and the global optimal position. The particle speed update formula is as follows: Among them, w1 is the inertia weight, m ​​is the current number of iterations, is the velocity of the jth particle in the next iteration cycle, is the speed of the jth particle in the current iteration cycle, is the position of the jth particle in the current iteration cycle, c3 and c4 are the individual learning factor and the group learning factor respectively, r3 and r4 are random numbers between [0,1], pBest j is the historical optimal position of the jth particle, and gBest1 is the global optimal position; S210) After the power optimization scheduling particle is updated, it is determined whether the speed of the two variables, the number of mass blocks in the particle and the battery energy storage power, exceeds the set speed limit. If it exceeds the upper limit, the speed of the variable is adjusted to If it exceeds the lower limit, the speed of the variable is adjusted to If the speed limit is not exceeded, the speed of the variable is not adjusted; S211) Update the position of each particle according to the individual historical optimal position and the global optimal position. The particle position update formula is as follows: in, is the position of the jth particle in the next iteration cycle; S212), determine each power optimization scheduling particle (n, p b ) Whether the updated position exceeds the set constraints, if the number of mass blocks n exceeds the upper limit of the number of mass blocks n max Then let the number of mass blocks in the particle n = n max , if the number of mass blocks exceeds the lower limit n min Then let the number of mass blocks in the particle n = n min , if n does not exceed the mass block constraint condition, it will not change. Similarly, if the battery energy storage power p b Exceeded the battery power limit p b,max Then let the battery energy storage power p in the particle be b =p b,max If the battery power limit is exceeded -p b,max Then let the battery energy storage power p in the particle be b =-p b,max , if p b If the battery power constraint is not exceeded, no change will occur; S213), determine whether the number of iterations has reached the maximum number of iterations at this time, if it has reached it, execute step S213, if not, set m=m+1, and execute step S206; S214), output the optimal power of gravity energy storage and battery energy storage and calculate the operating cost within the cycle, update the capacity of gravity energy storage and battery energy storage at this time, and set i=i+1, and execute step S203; S215) Calculate the operating cost within the scheduling period T and return it to the upper layer capacity optimization configuration algorithm.

3. The method for optimizing the dual-layer capacity of a hybrid energy storage system for gravity energy storage and battery energy storage according to claim 1, characterized in that: In step S203, the maximum number of mass blocks and the minimum number of mass blocks that can be adjusted in the multi-track gravity energy storage system in the current cycle are detected. The specific steps are: S301) Read the total number of mass blocks n1 on all tracks in the previous cycle, initialize the track number h, and set n max =n min =n1; S302), detecting whether the crane on the hth track can perform the operation of adding or removing the mass block, if yes, executing step S303, if not, executing step S304; S303) Detect whether there is a mass block at the end of the current running direction of the hth track. If so, the upper limit of the number of mass blocks on the slope track is n. max Unchanged, lower limit n min minus 1; If there is no mass block, set the upper limit of the number of mass blocks n max Add 1, lower limit n min constant; S304) Detect whether there is a mass block at the starting end of the current motion direction of the hth track. If so, the upper limit of the number of mass blocks n max Minus 1, if it does not exist, the upper limit of the number of mass blocks n max constant; S305) Determine whether all tracks have been detected, and if so, output the upper limit n of the number of mass blocks. max and lower limit n min Otherwise, let h=h+1 and re-execute steps S302, S303, and S304.

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