Multi-objective optimization method for hybrid energy storage capacity configuration for power fluctuation mitigation

Abstract

The application relates to a hybrid energy storage capacity multi-objective optimization configuration method for power fluctuation suppression, determines grid-connected unbalanced power, establishes an OVMD mathematical model, determines an optimal modal decomposition layer number K, decomposes the grid-connected unbalanced power signal, and determines high and low frequency reconstruction component orders and a demarcation point of connotation modal components in combination with superconducting magnet charging and discharging cycle time, determines low frequency power components and high frequency components, takes the minimum full life cycle cost of the hybrid energy storage system and the minimum operation penalty cost as targets, establishes a superconducting-electrochemical hybrid energy storage capacity multi-objective configuration model, obtains a Pareto front solution set of optimization target parameters, and obtains a superconducting-electrochemical hybrid energy storage optimal configuration scheme. The beneficial effects are that the influence of subjective K value setting on the decomposition layer number is reduced, the applicability of VMD is improved, the global search capability is enhanced, the population diversity is increased, the algorithm convergence is improved, and the uniformity of the optimal solution distribution of the Pareto front is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hybrid energy storage, in particular to a hybrid energy storage capacity multi-objective optimization configuration method for power fluctuation suppression. BACKGROUND

[0002] Under the background of the double carbon target, the green and low-carbon transformation of electric power is accelerating, and the proportion of new energy installed capacity is constantly breaking new highs. However, wind and light, and other new energy are affected by the environment, and the power generation output has high intermittency and volatility. Large-scale fluctuating new energy connected to the power grid brings serious challenges to the safe and stable operation of the power grid, and is often accompanied by the phenomenon of abandoned wind and light. With the development of new energy storage technology, by connecting energy storage devices to wind and light generation systems, and rapidly exchanging power with the power grid, the power and voltage fluctuations caused by the grid-connected new energy can be effectively suppressed, and the efficiency of the power grid and the new energy consumption rate can be improved. Single energy or power storage cannot meet the compensation needs of high-frequency power fluctuations and low-frequency power fluctuations, and hybrid energy storage (HESS) combines the characteristics of superconducting instantaneous high-power throughput and battery long-time energy support. By using battery energy storage (BES) to bear the main low-frequency component of wind power and photovoltaic power generation fluctuation suppression, it can provide long-time power throughput, and by using superconducting magnet energy storage (SMES) to bear the high-frequency fluctuation component, it is beneficial to prolong the service life of battery energy storage. Compared with single-form energy storage, it is more suitable for new energy grid-connected fluctuation suppression in complex fluctuation conditions.

[0003] The capacity configuration planning of hybrid energy storage is directly related to the investment cost of the energy storage system, the power suppression effect, and the service life of electrochemical energy storage. If the capacity configuration of superconducting magnets and batteries is too much, the system benefit cannot be maximized, and the cost is wasted. If the capacity configuration of superconducting magnets and batteries is too little, it will affect the power suppression effect and system reliability, and also affect the service life of the battery. In order to balance the benefit return and cost investment of energy storage, and improve the utilization efficiency of the energy storage system, reasonable capacity configuration is very necessary.

[0004] Currently, most hybrid energy storage capacity configuration methods aim to minimize the lifecycle cost of the energy storage system. They utilize a low-pass filter (LPF) to decompose unbalanced power signals, compensating low-frequency power fluctuations with electrochemical energy storage and high-frequency power fluctuations with superconducting magnet energy storage. However, low-pass filtering methods have a fixed time constant, making them difficult to handle complex and variable unbalanced power scenarios. Furthermore, LPFs have difficult-to-eliminate time delays, which can lead to over-configuration of energy storage system capacity. Methods based on empirical mode decomposition (EMD) are prone to aliasing of decomposed modes. Furthermore, optimizing hybrid energy storage capacity often requires consideration of both economics and operational stability. Capacity configuration models based solely on lifecycle cost are inaccurate and ignore factors such as the constraints imposed by system stability on energy storage. This can lead to curtailment of wind and solar power generation even after energy storage is configured. Summary of the Invention

[0005] In order to solve the problems of large errors in the hybrid energy storage capacity configuration model and unreasonable output distribution in the above-mentioned prior art, this paper proposes a multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing.

[0006] The present invention solves the above-mentioned technical problem with the following technical solution: a multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing, comprising the following steps:

[0007] Step 1: Determine the unbalanced power of the grid;

[0008] Step 2, establish the OVMD mathematical model;

[0009] Step 3. Determine the optimal decomposition layer number K, bring the grid-connected unbalanced power data into the OVMD mathematical model to obtain the intrinsic modal components after the decomposition of the grid-connected unbalanced power data, and combine the superconducting magnet charging and discharging cycle time to determine the high-frequency and low-frequency reconstruction component orders and demarcation points of the intrinsic modal components. Then compare the sizes of the frequencies of the intrinsic modal components and the demarcation frequencies. When the frequency of a certain intrinsic modal component is lower than the demarcation frequency, the frequency of the intrinsic modal component and the frequencies of the other intrinsic modal components after it are used as low-frequency power components, and the frequency of the intrinsic modal component before the frequency of the intrinsic modal component is used as high-frequency components. The low-frequency power components are used as charging and discharging power instructions for battery energy storage, and the high-frequency components are used as charging and discharging instructions for superconducting magnets.

[0010] Step 4: With the goal of minimizing the full life cycle cost of hybrid energy storage and the cost of power curtailment and backup power purchase, and considering the capacity constraints, power constraints, and SOC constraints of the hybrid energy storage system, a multi-objective configuration model for superconducting-electrochemical hybrid energy storage capacity is established;

[0011] Step 5: Use a fast non-dominated sorting genetic algorithm based on an elite strategy to optimize and solve the multi-objective configuration model of superconducting-electrochemical hybrid energy storage capacity;

[0012] First, within the feasible solution range of the optimization target parameter, a uniformly distributed initial population is randomly generated in the solution space through the good point set. Second, the best individuals that emerge during the population re-evolution are directly copied to the next generation without genetic manipulation to obtain the Pareto front solution set for the optimization target parameter, that is, the optimal solution set of the objective function.

[0013] Step 6: Generate the optimal configuration scheme for superconducting-electrochemical hybrid energy storage.

[0014] On the basis of the above technical solution, the present invention can also be improved as follows.

[0015] Furthermore, the grid-connected unbalanced power in Step 1 is the wind power grid-connected unbalanced power or the photovoltaic grid-connected unbalanced power.

[0016] Furthermore, taking the unbalanced power of wind power in Step 1 as an example, the specific steps of Step 1 are:

[0017] Obtain historical power data of wind farm operation;

[0018] Based on the planned grid-connected power of wind power construction and the national standard for grid-connected power fluctuation limits, combined with the planned grid-connected power of the wind farm, the unbalanced power of wind power grid connection is calculated;

[0019] The calculation formula is:

[0020]

[0021] Among them, P unb (t) is the unbalanced power of wind power connected to the grid at time t; P ref (t) is the planned grid-connected power of the wind farm at time t, P wind (t) is the actual output power of the wind farm at time t; ΔP wind (t) is the wind power output power fluctuation value allowed at time t;

[0022]

[0023] Where ΔP wind (t) is the fluctuation of wind power active power at time t; P thr is the active power fluctuation limit of the wind farm within the set dispatch time window L. The parameter calculations are as follows:

[0024]

[0025] Pthreshold =P N ×r%

[0026] Where m is the number of fluctuation intervals, which is calculated based on the scheduling time window L and the sampling time window Δt. d is the maximum fluctuation interval, P N is the rated installed capacity of wind power generation, and r is the maximum limit of wind power active power fluctuation that meets the wind power grid connection standard.

[0027] Furthermore, the method for determining the optimal number of decomposition layers K in Step 3 is:

[0028] Step 31, VMD uses iterative search for the optimal solution of the variational model to determine the component center frequency and bandwidth of each mode;

[0029] The model iterative update formula is:

[0030]

[0031] The model constraints are:

[0032]

[0033] Among them, u k ={u1,u2,...,u k} are the modal basis functions; f(ω), u i (ω), is the Fourier transform result corresponding to the original signal, each modal signal and Lagrange multiplier, K is the number of modal decomposition layers, that is, the number of final modes, for The center frequency of the current mode in , α is The quadratic penalty factor, ω k ={ω1,ω2,...,ω k} is the center frequency of each mode, δ is the Dirkat distribution;

[0034] Step 32: Starting from K=1, continuously increase the K value iteratively. Perform a calculation every time a K value is input. The K value determines the number of center frequencies after decomposition. If the final frequencies decomposed by the current and last two K values ​​are basically the same during the iteration process, then the K-1 value is the optimal modal decomposition layer number K.

[0035] Furthermore, the demarcation frequency in Step 4 is:

[0036]

[0037] Wherein, T is the charge and discharge time within the safe range of the superconducting magnet SOC, in minutes;

[0038] Assuming that the frequency of the Nth intrinsic modal component is less than the set boundary frequency, then:

[0039] k=N,ω N ≤f H

[0040]

[0041] Among them, P SMES (t) is the target suppression power of superconducting energy storage, P BAT (t) is the target smoothing power of battery energy storage, IMF is the intrinsic modal components obtained by decomposition of the OVMD mathematical model, and K is the number of modes corresponding to the boundary frequency.

[0042] Furthermore, the multi-objective configuration model of superconducting-electrochemical hybrid energy storage capacity established in Step 4 is:

[0043] minF=[f1,f2] T

[0044]

[0045] Among them, f1 is the full life cycle energy storage cost, f2 is the power curtailment penalty cost caused by unreasonable configuration of hybrid energy storage capacity during grid-connected operation, and the additional power purchase cost caused by insufficient hybrid energy storage capacity when smoothing fluctuations. is the maximum charging and discharging power of the hybrid energy storage, is the remaining capacity of hybrid energy storage at time t, is the maximum limit of hybrid energy storage capacity, is the hybrid energy storage output power at time t, is the current state of charge of the hybrid energy storage, are the minimum and maximum values ​​of the hybrid energy storage state of charge constraints, respectively.

[0046] Further, Step 5 is as follows:

[0047] Step 51: Within the feasible solution range of the optimization target parameter, randomly generate a uniformly distributed initial population in the solution space through the good point set;

[0048] Step 52: Perform mutation and crossover operations on the initial population;

[0049] Step 53: Perform non-dominated sorting and crowding calculation on the population.

[0050] Furthermore, Step 51 is specifically as follows:

[0051] Assume that the spatial dimension of the population is n, the population size is m, and calculate the r value, r=(r1,r2...,r n ),in:

[0052] m i represents the i-th individual;

[0053] Construct a good point set of population size m: P n (i)={(r1i1,r2i2...,r n i n )},i=1,2,3,...,n;

[0054] P n Mapping to the feasible region where the population is located: Among them, a j Indicates the lower limit of the current dimension, b j Indicates the upper limit of the current dimension.

[0055] Furthermore, Step 52 is specifically as follows:

[0056] For the i-th individual vector X in the s-th generation population ij,s , and its mutation operation is:

[0057] V ij,s+1 =X r1,s +F×(X r2,s -X r3,s )

[0058] Among them, V ij,s+1 is the individual vector after mutation, r1, r2, r3 are three different random numbers in (1, 2, ..., m) and are also different from the current individual position, X r1,s 、X r2,s 、X r3,s They represent the individual vectors at positions r1, r2, and r3 in the s generation population, respectively. The mutation operator F∈[0.5,1] is used to control the differential vector X r1,s -X r2,s the degree of magnification;

[0059] The intermediate population generated by the crossover operation is shown below:

[0060]

[0061] Among them, U ij,s+1 is the new individual generated after crossover, C R is the crossover probability between [0,1], and rand(0,1) is a random number between [0,1];

[0062] When rand(0,1)<C R When the new individual U ij,s+1 The j-th variable value is the variant individual V ij,s+1 The jth variable in ;

[0063] When rand(0,1)>C R When the new individual U ij,s+1 The j-th variable value comes from the original individual U ij,s+1 The jth variable in ;

[0064] Calculate the original population X ij,s and the mutated population U ij,s The two objective function (f1, f2) values ​​corresponding to each individual are obtained by substituting the individual vector into the established superconducting-electrochemical hybrid energy storage capacity multi-objective configuration model, and calculating the two objective function values ​​corresponding to each individual.

[0065] Furthermore, Step 53 is specifically as follows:

[0066] 1) The original population X ij,s and the newly generated population U ij,s+1 Merge into a population R i , population size is 2m;

[0067] Let i = 1, k = 1, 2, ..., 2m, and the individual R i With different individuals R k Compare the two objective function values ​​​​with each other to judge the individual R i With individual R k the relationship of domination and non-domination between them;

[0068] If there is no individual R k Both objective functions are better than R i , then mark R i is a non-dominated individual, let i = i + 1, until the population R is found i All non-dominated individuals, i.e. the first non-dominated layer F(1) of the population;

[0069] Then exclude the marked non-dominated individuals and conduct the next round of comparison to obtain the second non-dominated layer F(2);

[0070] And so on, until the entire population of individuals completes the non-dominated hierarchy sorting.

[0071] 2) Sort the elements in F(i) in descending order according to the crowding distance, and add the top elements after sorting to P(t+1) until the size of P(t+1) is N;

[0072] Perform selection, crossover, and mutation on P(t+1) to generate a new population Q(t+1). The new population Q(t+1) is combined with the parent Q(t) to form a new population R i+1 ;

[0073] The formula for calculating congestion is:

[0074]

[0075] Among them, Dis(R k ) is the individual R k The congestion degree, f m (k+1),f m (k-1) is R k The mth objective function value of two adjacent individuals, parameter is the maximum and minimum value of the mth objective function;

[0076] 3) Repeat step 2) until the algorithm termination condition is met, that is, the maximum number of iterations is reached.

[0077] The beneficial effects of the present invention are:

[0078] Taking into account the inherent delays and energy aliasing present in conventional low-pass filtering and empirical mode decomposition methods for unbalanced signal decomposition, an optimal variational mode decomposition (OVMD) is proposed to reduce the impact of subjectively set K values ​​on the number of decomposition levels and improve the applicability of VMD. Furthermore, to address the problem of genetic algorithms easily falling into local optimality, a set of good points is used to generate a uniformly distributed initial population within the solution space. Non-dominated sorting and elitist strategies are added to the traditional differential evolution algorithm of the genetic algorithm to avoid local convergence in the optimization process, enhance global search capabilities, increase population diversity, improve algorithm convergence, and improve the uniformity of the distribution of optimal solutions along the Pareto front. Furthermore, with the goals of minimizing the full lifecycle cost of hybrid energy storage and minimizing the cost of curtailed power and backup power purchases, the present invention establishes a multi-objective configuration model for superconducting-electrochemical hybrid energy storage capacity. The frequencies of the intrinsic modal components of the unbalanced power after OVMD decomposition are decoupled into low-frequency power components and high-frequency components, which serve as charging and discharging power instructions for batteries and supercapacitors, respectively, to achieve an economical and reasonable energy storage capacity configuration result. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a flow chart of the multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing in the present invention;

[0080] Figure 2 This is the original power diagram of wind power generation in the wind farm of the present invention;

[0081] Figure 3 The grid-connected power diagram considering the allowable power fluctuation of wind power;

[0082] Figure 4 The unbalanced power is the original power minus the grid-connected power, i.e. the power diagram that the energy storage needs to compensate;

[0083] Figure 5 It is the frequency diagram of each intrinsic modal component of the unbalanced power in the present invention;

[0084] Figure 6 The Pareto frontier graph of the objective function optimized in the present invention;

[0085] Figure 7 This is a diagram of the SOC state changes of the two energy storage components during the compensation process. DETAILED DESCRIPTION

[0086] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0087] Example 1

[0088] like Figure 1 、 Figure 4 、 Figure 5 、 Figure 6 、 Figure 7 As shown in FIG, a multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing includes the following steps:

[0089] Step 1: Determine the unbalanced power of the grid;

[0090] Step 2, establish the OVMD mathematical model (optimal variational mode decomposition mathematical model);

[0091] Step 3. Determine the optimal decomposition layer K, bring the grid-connected unbalanced power data into the OVMD mathematical model to obtain the intrinsic modal components (IMF) after the decomposition of the grid-connected unbalanced power data, and combine the superconducting magnet charging and discharging cycle time to determine the order and demarcation point of the high- and low-frequency reconstruction components of the intrinsic modal components. The order of high- and low-frequency components refers to the number of signals after the signal is decomposed, that is, Figure 4 After decomposing the signal, we get Figure 5 The number of signals in the middle signal, the demarcation point is the subsequent demarcation frequency, and then the size of each intrinsic modal component frequency and the demarcation frequency is compared. When the frequency of a certain intrinsic modal component is lower than the demarcation frequency, the intrinsic modal component frequency and the subsequent intrinsic modal component frequencies are used as low-frequency power components, and the intrinsic modal component frequency before the intrinsic modal component frequency is used as high-frequency components. The low-frequency power component is used as the charge and discharge power instruction of the battery energy storage, and the high-frequency component is used as the charge and discharge instruction of the superconducting magnet.

[0092] Step 4: With the goal of minimizing the full life cycle cost of hybrid energy storage and the cost of power curtailment and backup power purchase, and considering the capacity constraints, power constraints, and SOC constraints of the hybrid energy storage system, a multi-objective configuration model for superconducting-electrochemical hybrid energy storage capacity is established;

[0093] Step 5: Use a fast non-dominated sorting genetic algorithm based on an elite strategy to optimize and solve the multi-objective configuration model of superconducting-electrochemical hybrid energy storage capacity;

[0094] First, within the range of feasible solutions for the optimization target parameters, a uniformly distributed initial population is randomly generated in the solution space through the good point set. Second, the best individuals that emerge during the population re-evolution are directly copied to the next generation without genetic manipulation to obtain the Pareto front solution set for the optimization target parameters, that is, the optimal solution set of the objective function. The solution in it is the optimal solution.

[0095] Step 6. Generate the optimal configuration scheme for superconducting-electrochemical hybrid energy storage.

[0096] Example 2

[0097] like Figure 2 、 Figure 3 、 Figure 4 As shown, this embodiment is a further optimization based on embodiment 1, specifically as follows:

[0098] The grid-connected unbalanced power in Step 1 is the wind power grid-connected unbalanced power or the photovoltaic grid-connected unbalanced power;

[0099] Since the subsequent configuration method of wind power grid-connected unbalanced power is the same as that of photovoltaic grid-connected unbalanced power, the grid-connected unbalanced power in Step 1 is described as wind power grid-connected unbalanced power. The specific steps of Step 1 are:

[0100] First, obtain the historical power data of wind farm operation;

[0101] Then, based on the planned grid-connected power of wind power construction and the national standard for grid-connected power fluctuation limits, and combined with the planned grid-connected power of the wind farm, the unbalanced power of wind power grid connection is calculated;

[0102] The calculation formula is:

[0103]

[0104] Among them, P unb (t) is the unbalanced power of wind power connected to the grid at time t; P ref (t) is the planned grid-connected power of the wind farm at time t, P wind (t) is the actual output power of the wind farm at time t; ΔP wind (t) is the wind power output power fluctuation value allowed at time t;

[0105]

[0106] Where ΔP wind (t) is the fluctuation of wind power active power at time t, P thris the active power fluctuation limit of the wind farm within the set dispatch time window L. The two parameters are calculated as follows:

[0107]

[0108] P threshold =P N ×r%

[0109] Where m is the number of fluctuation intervals, which is calculated based on the scheduling time window L and the sampling time window Δt. d is the maximum fluctuation interval, P N is the rated installed capacity of wind power generation, and r is the maximum limit of wind power active power fluctuation that meets the wind power grid connection standard.

[0110] Example 3

[0111] This embodiment is a further optimization based on embodiment 1 or 2, specifically as follows:

[0112] The method for determining the optimal number of decomposition layers K in Step 3 is:

[0113] Step 31: First, VMD uses iterative search for the optimal solution of the variational model to determine the component center frequency and bandwidth of each mode, so that each mode is smooth after demodulation to baseband;

[0114] The model iterative update formula is:

[0115]

[0116] The model constraints are:

[0117]

[0118] Among them, u k ={u1,u2,...,u k} are the modal basis functions; f(ω), u i (ω), is the Fourier transform result corresponding to the original signal, each modal signal and Lagrange multiplier, K is the number of modal decomposition layers, that is, the number of final modes, for The center frequency of the current mode in , α is The quadratic penalty factor, ω k ={ω1,ω2,...,ω k} is the center frequency of each mode, δ is the Dirkat distribution;

[0119] Step 32. Set it to start from K=1 and continuously increase the K value iteratively. Perform a calculation every time a K value is entered. The number of center frequencies after decomposition depends on the K value. If the final frequencies decomposed by the current and last two K values ​​are basically the same during the iteration process, then the K-1 value is the optimal modal decomposition layer number K.

[0120] Example 4

[0121] like Figure 5 As shown, this embodiment is a further optimization based on embodiment 3, specifically as follows:

[0122] The demarcation frequency in Step 3 is:

[0123]

[0124] Wherein, T is the charge and discharge time within the safe range of the superconducting magnet SOC, in minutes;

[0125] Assuming that the frequency of the Nth intrinsic modal component is less than the set boundary frequency, then:

[0126] k=N,ω N ≤f H

[0127]

[0128] Among them, P SMES (t) is the target suppression power of superconducting energy storage, P BAT (t) is the target power of battery energy storage, and IMF is the intrinsic modal components decomposed by the OVMD mathematical model, that is, Figure 5 The SMES compensates the high-frequency signal by adding up all the high-frequency signals according to the high- and low-frequency dividing point determined in Step 3 above, and determines the high-frequency signal compensated by SMES. K is the number of modes corresponding to the dividing frequency.

[0129] Example 5

[0130] This embodiment is a further optimization based on embodiment 3, specifically as follows:

[0131] The multi-objective configuration model of superconducting-electrochemical hybrid energy storage capacity established in Step 5 is:

[0132] minF=[f1,f2] T

[0133]

[0134] Among them, f1 is the full life cycle energy storage cost, f2 is the power curtailment penalty cost caused by unreasonable configuration of hybrid energy storage capacity during grid-connected operation, and the additional power purchase cost caused by insufficient hybrid energy storage capacity when smoothing fluctuations. is the maximum charging and discharging power of the hybrid energy storage, is the remaining capacity of hybrid energy storage at time t, is the maximum limit of hybrid energy storage capacity, is the hybrid energy storage output power at time t, is the current state of charge of the hybrid energy storage, are the minimum and maximum values ​​of the hybrid energy storage state of charge constraints respectively;

[0135] The objective function minf1 is:

[0136] minf1=C HESS =C inv +C o&m +C rep -C res

[0137] C inv The initial one-time investment cost of the hybrid energy storage system, which consists of the cost of the energy storage device itself, the power conversion unit cost, and the auxiliary equipment cost;

[0138]

[0139] Among them, C bat 、C smes are the costs of battery energy storage and superconducting energy storage respectively, C bat_bop 、C smes_bop are the costs of battery energy storage auxiliary equipment and superconducting energy storage auxiliary equipment, C bat_pcs 、C smes_pcs are the unit power costs of battery energy storage and superconducting energy storage power converters, r is the discount rate, and the operating cycle is y years;

[0140] C bat =C E_bat E bat_rate +C P_bat P bat_rate

[0141] C smes =C E_smes E smes_rate +C P_smes P smes_rate

[0142] Among them, C E_bat 、C E_smes are the unit capacity costs of battery energy storage and superconducting energy storage, C P_bat 、C P_smesare the unit power costs of battery energy storage and superconducting energy storage respectively; E bat_rate 、P bat_rate 、E smes_rate 、P smes_rate are optimization variables, representing the rated installed capacity and power of electrochemical energy storage and the rated installed capacity and power of superconducting energy storage;

[0143] C bat_bop =C unit_bop E bat_rate

[0144] C bat_sop =C unit_sop P smes_rate

[0145] Among them, C unit_bop is the unit capacity cost of battery energy storage auxiliary equipment, C unit_sop The unit power cost of superconducting energy storage auxiliary equipment;

[0146] C bat_pcs =C pcs_bat P bat_rate

[0147] C smes_pcs =C pcs_smes P bat_rate

[0148] Among them, C pcs_bat 、C pcs_smes They are the unit power costs of battery energy storage and superconducting energy storage power converters respectively;

[0149] The operation and maintenance costs of a hybrid energy storage system primarily consist of fixed costs and variable costs. Fixed costs include daily operation labor and maintenance costs, which are related to the energy storage system's operating time and capacity. Variable costs primarily refer to maintenance costs incurred due to system failures caused by internal or external factors. Both costs can be converted into a proportional coefficient related to the energy storage system's configured capacity based on experience.

[0150]

[0151] Among them, C o&m is the operation and maintenance cost, E rate is the total capacity of the hybrid energy storage system, γ1 is the unit capacity operation and maintenance cost coefficient of battery energy storage, and γ2 is the unit capacity operation and maintenance cost coefficient of superconducting energy storage;

[0152] Since the cycle life of superconducting energy storage is much longer than that of battery energy storage, the hybrid energy storage system also involves the replacement cost of energy storage components during operation:

[0153]

[0154] Among them, C rep is the replacement cost of the battery energy storage; m is the number of times the battery energy storage is replaced during the life cycle of the hybrid energy storage system;

[0155] Residual value cost of hybrid energy storage system C res :

[0156]

[0157] Among them, σ res is the salvage value recovery rate;

[0158] The objective function minf2 is the power curtailment penalty cost caused by unreasonable configuration of hybrid energy storage capacity during grid-connected operation and the additional power purchase cost caused by insufficient hybrid energy storage capacity during fluctuation smoothing:

[0159] minf2=C penalty =α1E critical +α2E shortage

[0160]

[0161] E critical is the power abandonment capacity, α1 is the power abandonment penalty coefficient, E shortage is the additional power purchase capacity when the hybrid energy storage capacity is insufficient, and α2 is the power purchase penalty coefficient. Hybrid energy storage constraints:

[0162] 1) Remaining power constraints of energy storage systems

[0163]

[0164] 2) Power constraints of energy storage systems

[0165]

[0166] 3) State of charge constraints

[0167]

[0168] Example 6

[0169] This embodiment is a further optimization based on any one of Embodiments 1 to 5, and is specifically as follows:

[0170] Step 5 is as follows:

[0171] Step 51: Within the feasible solution range of the optimization target parameter, randomly generate a uniformly distributed initial population in the solution space through the good point set;

[0172] Step 52: Perform mutation and crossover operations on the initial population;

[0173] Step 53: Perform non-dominated sorting and crowding calculation on the population.

[0174] Among them, Step 51 is specifically as follows:

[0175] Assume that the spatial dimension of the population is n, the population size is m, and calculate the r value, r=(r1,r2...,r n ),in:

[0176] m i represents the i-th individual;

[0177] Construct a good point set of population size m: P n (i)={(r1i1,r2i2...,r n i n )},i=1,2,3,...,n;

[0178] P n Mapping to the feasible region where the population is located: Among them, a j Indicates the lower limit of the current dimension, b j Indicates the upper limit of the current dimension.

[0179] Step 52 is as follows:

[0180] For the i-th individual vector X in the s-th generation population ij,s , and its mutation operation is:

[0181] V ij,s+1 =X r1,s +F×(X r2,s -X r3,s )

[0182] Among them, V ij,s+1 is the individual vector after mutation, r1, r2, r3 are three different random numbers in (1, 2, ..., m) and are also different from the current individual position, X r1,s 、X r2,s 、X r3,s They represent the individual vectors at positions r1, r2, and r3 in the s generation population, respectively. The mutation operator F∈[0.5,1] is used to control the differential vector X r1,s -X r2,s the degree of magnification;

[0183] The intermediate population generated by the crossover operation is shown below:

[0184]

[0185] Among them, U ij,s+1is the new individual generated after crossover, C R is the crossover probability between [0,1], and rand(0,1) is a random number between [0,1];

[0186] When rand(0,1)<C R When the new individual U ij,s+1 The j-th variable value is the variant individual V ij,s+1 The jth variable in ;

[0187] When rand(0,1)>C R When the new individual U ij,s+1 The j-th variable value comes from the original individual U ij,s+1 The jth variable in ;

[0188] Calculate the original population X ij,s and the mutated population U ij,s The two objective function (f1, f2) values ​​corresponding to each individual are obtained by substituting the individual vector into the established superconducting-electrochemical hybrid energy storage capacity multi-objective configuration model, and calculating the two objective function values ​​corresponding to each individual.

[0189] Finally, Step 53 is as follows:

[0190] 1) The original population X ij,s and the newly generated population U ij,s+1 Merge into a population R i , population size is 2m;

[0191] Let i = 1, k = 1, 2, ..., 2m, and the individual R i With different individuals R k Compare the two objective function values ​​​​with each other to judge the individual R i With individual R k the relationship of domination and non-domination between them;

[0192] If there is no individual R k Both objective functions are better than R i , then mark R i is a non-dominated individual, let i = i + 1, until the population R is found i All non-dominated individuals, i.e. the first non-dominated layer F(1) of the population;

[0193] Then exclude the marked non-dominated individuals and conduct the next round of comparison to obtain the second non-dominated layer F(2);

[0194] And so on, until the entire population of individuals completes the non-dominated hierarchy sorting.

[0195] 2) Sort the elements in F(i) in descending order according to the crowding distance, and add the top elements after sorting to P(t+1) until the size of P(t+1) is N;

[0196] Perform selection, crossover, and mutation on P(t+1) to generate a new population Q(t+1). The new population Q(t+1) is combined with the parent Q(t) to form a new population R i+1 ;

[0197] The formula for calculating congestion is:

[0198]

[0199] Among them, Dis(R k ) is the individual R k The congestion degree, f m (k+1),f m (k-1) is R k The mth objective function value of two adjacent individuals, parameter is the maximum and minimum value of the mth objective function;

[0200] 3) Repeat step 2) until the algorithm termination condition is met, that is, the maximum number of iterations is reached.

[0201] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing, characterized by: The steps include: Step 1: Determine the unbalanced power of the grid; Step 2, establish the OVMD mathematical model; Step 3. Determine the optimal decomposition layer number K, bring the grid-connected unbalanced power data into the OVMD mathematical model to obtain the intrinsic modal components after the decomposition of the grid-connected unbalanced power data, and combine the superconducting magnet charging and discharging cycle time to determine the high-frequency and low-frequency reconstruction component orders and demarcation points of the intrinsic modal components. Then compare the sizes of the frequencies of the intrinsic modal components and the demarcation frequencies. When the frequency of a certain intrinsic modal component is lower than the demarcation frequency, the frequency of the intrinsic modal component and the frequencies of the other intrinsic modal components after it are used as low-frequency power components, and the frequency of the intrinsic modal component before the frequency of the intrinsic modal component is used as high-frequency components. The low-frequency power components are used as charging and discharging power instructions for battery energy storage, and the high-frequency components are used as charging and discharging instructions for superconducting magnets. Step 4: With the goal of minimizing the full life cycle cost of hybrid energy storage and the cost of power curtailment and backup power purchase, and considering the capacity constraints, power constraints, and SOC constraints of the hybrid energy storage system, a multi-objective configuration model for superconducting-electrochemical hybrid energy storage capacity is established; Step 5: Use a fast non-dominated sorting genetic algorithm based on an elite strategy to optimize and solve the multi-objective configuration model of superconducting-electrochemical hybrid energy storage capacity; First, within the feasible solution range of the optimization target parameter, a uniformly distributed initial population is randomly generated in the solution space through the good point set. Second, the best individuals that emerge during the population re-evolution are directly copied to the next generation without genetic manipulation to obtain the Pareto front solution set for optimizing the target parameter, that is, the optimal solution set of the objective function. Step 6: Generate the optimal configuration scheme for superconducting-electrochemical hybrid energy storage.

2. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 1 is characterized in that: The grid-connected unbalanced power in Step 1 is the wind power grid-connected unbalanced power or the photovoltaic power grid-connected unbalanced power.

3. A multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 1 or 2, characterized in that: The method for determining the optimal number of decomposition layers K in Step 3 is: Step 31, VMD uses iterative search for the optimal solution of the variational model to determine the component center frequency and bandwidth of each mode; Step 32: Starting from K=1, continuously increase the K value iteratively. Perform a calculation every time a K value is input. The K value determines the number of center frequencies after decomposition. If the final frequencies decomposed by the current and last two K values ​​are basically the same during the iteration process, then the K-1 value is the optimal modal decomposition layer number K.

4. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 1 is characterized in that: The demarcation frequency in Step 3 is: Wherein, T is the charge and discharge time within the safe range of the superconducting magnet SOC, in minutes; Assuming that the frequency of the Nth intrinsic modal component is less than the set boundary frequency, then: k=N,ω N ≤f H Among them, P SMES (t) is the target suppression power of superconducting energy storage, P BAT (t) is the target smoothing power of battery energy storage, IMF is the intrinsic modal components decomposed by the OVMD mathematical model, and N is the number of modes corresponding to the demarcation frequency.

5. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 1 is characterized in that: The multi-objective configuration model of superconducting-electrochemical hybrid energy storage capacity established in Step 4 is: minF=[f1,f2] T Among them, f1 is the full life cycle energy storage cost, f2 is the power curtailment penalty cost caused by unreasonable configuration of hybrid energy storage capacity during grid-connected operation, and the additional power purchase cost caused by insufficient hybrid energy storage capacity when smoothing fluctuations. is the maximum charging and discharging power of the hybrid energy storage, is the remaining capacity of hybrid energy storage at time t, is the maximum limit of hybrid energy storage capacity, is the hybrid energy storage output power at time t, is the current state of charge of the hybrid energy storage, are the minimum and maximum values ​​of the hybrid energy storage state of charge constraints, respectively.

6. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 5 is characterized in that: Step 5 is as follows: Step 51: Within the feasible solution range of the optimization target parameter, randomly generate a uniformly distributed initial population in the solution space through the good point set; Step 52: Perform mutation and crossover operations on the initial population; Step 53: Perform non-dominated sorting and crowding calculation on the population.

7. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 6, characterized in that: Step 51 is as follows: Assume that the spatial dimension of the population is n, the population size is m, and calculate the r value, r=(r1,r2...,r n ),in: m i represents the i-th individual; Construct a good point set of population size m: P n (i)={(r1i1,r2i2...,r n i n )},i=1,2,3,...,n; P n Mapping to the feasible region where the population is located: Among them, a j Indicates the lower limit of the current dimension, b j Indicates the upper limit of the current dimension.

8. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 7, characterized in that: Step 52 is as follows: For the i-th individual vector X in the s-th generation population ij,s , and its mutation operation is: V ij,s+1 =X r1,s +F×(X r2,s -X r3,s ) Among them, V ij,s+1 is the individual vector after mutation, r1, r2, r3 are three different random numbers within 1, 2, ..., m and are also different from the current individual position, X r1,s 、X r2,s 、X r3,s They represent the individual vectors at positions r1, r2, and r3 in the s generation population, respectively. The mutation operator F∈[0.5,1] is used to control the differential vector X r1,s -X r2,s the degree of magnification; The intermediate population generated by the crossover operation is shown below: Among them, U ij,s+1 is the new individual generated after crossover, C R is the crossover probability between [0,1], and rand(0,1) is a random number between [0,1]; When rand(0,1)<C R When the new individual U ij,s+1 The j-th variable value is the variant individual V ij,s+1 The jth variable in ; When rand(0,1)>C R When the new individual U ij,s+1 The j-th variable value comes from the original individual X ij,s The jth variable in ; Calculate the original population X ij,s and the mutated population U ij,s+1 The two objective function (f1, f2) values ​​corresponding to each individual are obtained by substituting the individual vector into the established superconducting-electrochemical hybrid energy storage capacity multi-objective configuration model, and calculating the two objective function values ​​corresponding to each individual.

9. The multi-objective optimization configuration method for hybrid energy storage capacity for power fluctuation smoothing according to claim 8, characterized in that: Step 53 is as follows: 1) The original population X ij,s and the newly generated population U ij,s+1 Merge into a population R i , population size is 2m; Let i = 1, k = 1, 2, ..., 2m, and the individual R i With different individuals R k Compare the two objective function values ​​​​with each other to judge the individual R i With individual R k the relationship of domination and non-domination between them; If there is no individual R k Both objective functions are better than R i , then mark R i is a non-dominated individual, let i = i + 1, until the population R is found i All non-dominated individuals, i.e. the first non-dominated layer F(1) of the population; Then exclude the marked non-dominated individuals and conduct the next round of comparison to obtain the second non-dominated layer F(2); And so on, until the entire population of individuals completes the non-dominated ranking; 2) Sort the elements in F(i) in descending order according to the crowding distance, and add the top elements after sorting to P(t+1) until the size of P(t+1) is N; Perform selection, crossover, and mutation on P(t+1) to generate a new population Q(t+1). The new population Q(t+1) is combined with the parent Q(t) to form a new population R i+1 ; The formula for calculating congestion is: Among them, Dis(R k ) is the individual R k The congestion degree, f m (k+1),f m (k-1) is R k The mth objective function value of two adjacent individuals, parameter is the maximum and minimum value of the mth objective function; 3) Repeat step 2) until the algorithm termination condition is met, that is, the maximum number of iterations is reached.

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