A hybrid energy storage system capacity optimization method

Abstract

The application relates to a hybrid energy storage system capacity optimization method. First, a multi-objective mathematical model of the hybrid energy storage system is established, which is characterized by the cost of the hybrid energy storage system and the minimum number of charge-discharge conversion times of the battery; then, the multi-objective optimization is converted into a single-objective optimization problem with weights through an entropy weight method, and an improved particle swarm algorithm is used to optimize the single-objective function with the weight coefficient, so that the optimal solution of the power and capacity of the hybrid energy storage system is obtained; finally, in order to measure the optimization effect of the hybrid energy storage system applied to the suppression of wind power fluctuation, the application defines the state of charge coefficient and the grid connection smoothing rate of the hybrid energy storage system as the evaluation indexes of the multi-objective optimization of the hybrid energy storage system.

Description

TECHNICAL FIELD

[0001] The present application relates to a hybrid energy storage system capacity optimization method. BACKGROUND

[0002] In recent years, wind power has developed rapidly, and its penetration level continues to rise. However, due to the intermittent and random nature of wind power, direct grid-connected operation of wind power systems can impact the safety and stability of the power system. One of the reasons for low wind energy utilization is that the volatility of wind power output significantly affects grid voltage, frequency and other parameters, which reduces the capacity reliability and dispatchability of the unit and causes serious curtailment of wind power. Therefore, it is necessary to smooth the fluctuations of wind power. Usually, energy storage systems are combined with wind power systems to suppress the volatility of wind power output and improve the scale of wind power grid connection. However, due to physical constraints, a single energy storage medium cannot meet both power and energy requirements, and the use of hybrid energy storage technology is an effective means of suppressing wind power fluctuations.

[0003] Since the smoothing effect of hybrid energy storage system on wind power fluctuation directly affects the size of wind power fluctuation, a reasonable power allocation strategy between different media of hybrid energy storage is crucial. Generally, the larger the capacity of the hybrid energy storage system selected, the better the smoothing effect on wind power output. However, the larger the capacity of the hybrid energy storage, the higher the design cost, resulting in a lower cost performance. Therefore, a hybrid energy storage system with high cost performance should have an appropriate capacity. The capacity of different types of energy storage devices must be reasonably selected and the corresponding control method must be designed to fully utilize their respective advantages, thereby optimizing the cost and service life of the hybrid energy storage system while meeting the requirements of wind power grid connection. SUMMARY

[0004] The present application aims to provide a hybrid energy storage system capacity optimization method, which aggregates multiple objectives in the model into a function through an entropy weight method to determine the weight coefficient method; and solves the single objective function of the hybrid energy storage system with constraints through an improved particle swarm algorithm to obtain the optimal solution of the capacity of the hybrid energy storage system.

[0005] To achieve the above-mentioned purpose, the technical solution of the present application is: a hybrid energy storage system capacity optimization method, first establishing a multi-objective mathematical model with the cost of the hybrid energy storage system and the minimum number of charge and discharge conversions of the battery; then, converting the multi-objective optimization into a single-objective optimization problem with weights through an entropy weight method, and optimizing the single-objective function with weights using an improved particle swarm algorithm to obtain the optimal solution of the power and capacity of the hybrid energy storage system; finally, defining the state of charge coefficient of the hybrid energy storage system and the grid connection smoothing rate as evaluation indexes of the multi-objective optimization of the hybrid energy storage system.

[0006] In an embodiment of the present application, the multi-objective mathematical model with the minimum cost of the hybrid energy storage system and the minimum number of charge-discharge conversion of the battery is implemented as follows:

[0007] 1) Establishing a cost of the hybrid energy storage system as one of the objective functions, and the rated power of the super capacitor and the battery and the rated capacity of the super capacitor and the battery as the decision variables, the expression is

[0008]

[0009] 2) Battery charge-discharge conversion number target

[0010] The number of cycles of the battery is used as another objective function of the hybrid energy storage system to constrain the number of charge-discharge conversion of the battery, and the expression is as follows:

[0011] N = f2 (P SCr ,Q SCr ,P Batr ,Q Batr ) (2)

[0012] In order to record the number of cycles of the battery, the charge-discharge state quantity state(t) is introduced; the number of charge-discharge conversion of the battery is obtained by calculating the number of changes of the value of the charge-discharge state quantity state(t); the duration of the charge-discharge state quantity state(t) is calculated to obtain the charge-discharge time of the battery once;

[0013]

[0014] In formula (1)-(3): P SCr , P Batr are the rated power of the super capacitor and the battery respectively; Q SCr , Q Batr are the rated capacity of the super capacitor and the battery respectively; C PSC , C PBat are the unit prices of the rated power of the super capacitor and the battery respectively; C QSC , C QBat are the unit prices of the rated capacity of the super capacitor and the battery respectively;

[0015] 3) Based on the established objective functions of the cost of the hybrid energy storage system and the number of charge-discharge conversion of the battery, the capacity of the hybrid energy storage device is optimized and configured with the minimum of each objective function as the target, the rated power and the rated capacity of the super capacitor and the battery are used as the decision variables, and a multi-objective optimization mathematical model of the hybrid energy storage system is established, as shown in the following formula:

[0016]

[0017] f1, f2 are respectively the target functions of the hybrid energy storage system cost and the number of charge-discharge conversion times of the battery.

[0018] In an embodiment of the present application, the specific implementation mode of converting the multi-objective optimization into a single-objective optimization problem with weights by the entropy weight method is as follows:

[0019] First, the weight coefficient λ of each sub-target function in the target function is determined by the entropy weight method i Then, the weighted sub-target functions are added, thereby converting the multi-objective optimization problem with constraints into a single-objective function optimization problem with positive coefficients and constraints, wherein the constraint condition is set as the set X, that is, the constraint condition of capacity optimization configuration, and then the optimal solution of the function is obtained by optimizing the function on the constraint set X; the multi-objective function of the hybrid energy storage system is aggregated into a single-objective function as follows:

[0020]

[0021] f1, f2 are respectively two sub-target functions in the target function, and the weight coefficient λ of each sub-target function in the target function i reflects the relative importance of the corresponding sub-target function in the overall evaluation.

[0022] In an embodiment of the present application, the weight coefficient λ of each sub-target function in the target function is determined by the entropy weight method i , and the specific implementation is as follows:

[0023] 1) An index system original matrix X=[a ij ] m×n is established, which contains m evaluation objects and n evaluation indexes.

[0024]

[0025] 2) Data standardization

[0026] In the established index system, the original data is processed by using a fuzzy quantization mode to obtain a standardized matrix K=[b ij ] m×n ; the fuzzy quantization expression is as follows:

[0027]

[0028] In the formula, b ij is the corresponding value of the element a ij in the matrix X after standardization; and a ·j is the original data of the index j corresponding to the m evaluation objects.

[0029] The standardized matrix K=[b ij] m×n , the proportion of the i-th evaluation object in the j-th evaluation index is calculated:

[0030]

[0031] The proportion matrix P = [p ij ] m×n ;

[0032] 3) the entropy value i j of the j-th index is calculated, and I = [i ij ] 1×n :

[0033]

[0034] wherein p ij = 0, p ij lnp ij = 0;

[0035] 4) the entropy weight of the j-th index is obtained as:

[0036]

[0037] The entropy weight matrix W = [w ij ] 1×n .

[0038] In an embodiment of the present application, the improved particle swarm algorithm is used to optimize the single-objective function with weight coefficients, and the specific implementation of obtaining the optimal solution of the power and capacity of the hybrid energy storage system is as follows:

[0039] 1) initialization of the population; the population particle size is set, and the particle velocity v (0) = [v1, v2, v3, v4] T and the particle position x (0) = [x1, x2, x3, x4] T in the population are randomly initialized; the particle position in the population represents the numerical values of the four decision variables under the constraint condition, i.e., the numerical values of the rated power of the super capacitor and the battery and the rated capacity of the super capacitor and the battery; wherein the values of the four decision variables should meet the constraint condition of capacity configuration;

[0040] 2) calculate the initial extreme value of the population; the initial position x (0) = [x1, x2, x3, x4] T of the particle is substituted into the built hybrid energy storage system model, and simulation is run; if the constraint condition cannot be met during the smoothing process, the particle is invalid, and a new particle needs to be generated and simulation is run again until the particle meets the constraint condition of capacity configuration; then the initial individual extreme value p best and the group extreme value g best;

[0041] 3) Update the position and velocity of particles; update the particles according to the following formula, and the newly generated particles also meet the constraint condition of particle capacity configuration, and the particles that do not meet the constraint condition need to be regenerated;

[0042] x i (t+1) = x i (t) + v i (t+1) t = 1, 2, …, T max

[0043] v i (t+1) = ωv i (t) + c1r1(p i.best -x i (t)) + c2r2(g best -x i (t)) t = 1, 2, …, T max

[0044] In the formula: v is the velocity of the particle, x is the position of the particle, p i.best is the individual extreme value, g best is the group extreme value, c1 and c2 are acceleration factors, t is the current iteration number, i is the i-th particle in the population, ω is the inertia weight, r1 and r2 are random numbers generated in the interval;

[0045] 4) Update the inertia weight of the algorithm according to formula (11);

[0046]

[0047] In the formula: ω min and ω max are the minimum and maximum values of the inertia weight constant ω respectively; f min and f avg are the minimum and average values of the current particle objective function respectively;

[0048] 5) Update the individual extreme value and group extreme value in the population: substitute the position x(t+1) = [x1, x2, x3, x4] T of the newly generated particle into the built hybrid energy storage system model and run the simulation; similarly, the particles that do not meet the constraint condition need to be regenerated; the individual extreme value p best and the group extreme value g best are obtained and compared with the individual extreme value and the group extreme value of the last generation of particles; if the individual extreme value p best and the group extreme value g bestIf better than the last generation, then it is taken as the current individual extreme value and population extreme value; otherwise, the individual extreme value and population extreme value of the last generation are kept as the current individual extreme value and population extreme value.

[0049] 6) According to the evolution generation number or the precision algorithm setting condition, it is judged whether the algorithm stops or continues to evolve; if the condition meets the requirement, the search is ended, and the optimal solution of the objective function, i.e., the population extreme value g, is output best ; if the condition does not meet the requirement, it is returned to continue to execute step 3).

[0050] In an embodiment of the present application, the specific implementation mode of defining the state of charge coefficient of the hybrid energy storage system and the grid connection smoothing rate as the evaluation index of the multi-objective optimization of the hybrid energy storage system is as follows:

[0051] Supposing that the time during which the hybrid energy storage system participates in the wind power fluctuation suppression is [t1, t n ], and the sampling time interval is 1s;

[0052] 1) The state of charge coefficient h1 of the hybrid energy storage system is defined as:

[0053]

[0054] The state of charge coefficient h1 of the hybrid energy storage system is a ratio of the sum of the distance between the real-time state of charge SOC of the super capacitor and the state of charge battery in the running time and the intermediate value SOC medu and the difference between the maximum state of charge SOC max and the minimum state of charge SOC min ; if the state of charge level is mostly in the intermediate state during the hybrid energy storage system suppression process, the hybrid energy storage system can absorb and release more energy, and has more available capacity for the wind power fluctuation suppression, and has stronger wind power fluctuation suppression capacity; the smaller the value of h1 is, the more points the state of charge of the hybrid energy storage system is close to the intermediate position in the running process, and the stronger the wind power fluctuation suppression capacity is.

[0055] 2) The grid connection smoothing rate h2 is defined as:

[0056]

[0057] P Wind , P SC , and P Bat are the powers of the wind power, the super capacitor, and the battery respectively, the value of the grid connection smoothing rate h2 is the absolute value variance var|ΔP Hybrid | of the power surplus amount ΔP Hybrid of the hybrid energy storage system which cannot be completely absorbed, and the wind power grid connection power P GridThe absolute value variance var|P Grid The ratio of the absolute value variance var|P Hybrid The smaller the value of h2 is, the less power the hybrid energy storage system cannot absorb, and the better the smoothing effect of the wind power fluctuation is.

[0058] In an embodiment of the present application, the constraint condition in the capacity optimization configuration process of the hybrid energy storage system is as follows:

[0059]

[0060] In the formula, P Hybrid (t), P Wind (t), P SC (t), P Bat (t), P Grid (t) respectively represent the power of the hybrid energy storage system, the wind power, the super capacitor, the battery and the wind power grid connection at the time t, SOC Bat (t), SOC SC (t) respectively represent the minimum charge value of the battery, the maximum charge value of the battery, the charge value of the battery at the time t, the minimum charge value of the super capacitor, the maximum charge value of the super capacitor, the charge value of the super capacitor at the time t, ΔP Grid,1min (t) represents the wind power grid connection fluctuation at the time t; ΔP Grid,30min (t) represents the wind power grid connection fluctuation at the time t; γ 1min is the maximum value of the wind power grid connection fluctuation; γ 30min is the maximum value of the wind power grid connection fluctuation; Q Bat is the battery capacity; Q SC is the super capacitor capacity; Q Bat.r is the rated battery capacity; Q SC.r is the rated super capacitor capacity.

[0061] Compared with the prior art, the present application has the following beneficial effects:

[0062] (1) Considering the economy of the hybrid energy storage system, the application establishes a cost of the hybrid energy storage system as a target function; in order to improve the service life of the system, the application establishes a target function of the number of charge and discharge conversion of the storage battery for limiting the number of charge and discharge conversion of the storage battery; therefore, the application takes the cost of the hybrid energy storage system and the number of charge and discharge conversion of the storage battery as a target function, takes the rated power and capacity of the super capacitor and the rated power and capacity of the storage battery as decision variables, and takes technical indexes as constraint conditions to establish a multi-objective function model of the hybrid energy storage system with constraint conditions.

[0063] (2) The application defines the state of charge coefficient h1 and the grid-connected smoothing rate h2 of the hybrid energy storage system as two evaluation indexes of the multi-objective optimization of the hybrid energy storage system, which are used to measure the smoothing effect of the hybrid energy storage system with different energy storage modes and control strategies on the wind power fluctuation power after capacity optimization configuration. Considering the self-limitation of the energy storage element and the requirement of wind power grid connection, constraint conditions of the capacity optimization configuration are formulated.

[0064] (3) The optimization of the multi-objective function is more complex than that of the single objective function, and the multi-objective optimization is converted into single objective optimization through the weighting method. The entropy weight method is used to determine the size of the weight coefficient, and the importance of the sub-objective function is objectively evaluated. Finally, the improved particle swarm algorithm is used to solve the single objective function with the weight coefficient to obtain the optimal solution of the capacity of the hybrid energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The improved particle swarm algorithm flowchart of the application is shown in the figure. DETAILED DESCRIPTION

[0066] The technical solutions of the application will be specifically described below with reference to the drawings.

[0067] The application first establishes a multi-objective mathematical model with the cost of the hybrid energy storage system and the number of charge and discharge conversion of the storage battery being minimum; then, the multi-objective optimization is converted into a single objective optimization problem with weight through the entropy weight method, and the improved particle swarm algorithm is used to optimize the single objective function with the weight coefficient to obtain the optimal solution of the power and capacity of the hybrid energy storage system; finally, in order to measure the optimization effect of the hybrid energy storage system in smoothing the wind power fluctuation, the application defines the state of charge coefficient h1 and the grid-connected smoothing rate h2 of the hybrid energy storage system as evaluation indexes of the multi-objective optimization of the hybrid energy storage system.

[0068] The larger the capacity of the hybrid energy storage system is, the better the smoothing effect is, however, the cost of the hybrid energy storage system and the smoothing effect are in a contradictory relationship, the larger the capacity is, the higher the cost of the hybrid energy storage system is. Therefore, considering the economy of the hybrid energy storage system, the application firstly establishes a cost of the hybrid energy storage system as a target function, and the rated power of the super capacitor and the battery and the rated capacity of the super capacitor and the battery are as decision variables, and the expression is

[0069]

[0070] (2) battery charge and discharge conversion times target

[0071] Since the cost of the hybrid energy storage system is affected by the service life of the hybrid energy storage system at present, the service life of the hybrid energy storage system composed of the super capacitor and the battery is mainly determined by the cycle number of the battery; therefore, the application takes the cycle number of the battery as a target function of the hybrid energy storage system, and uses it to constrain the charge and discharge conversion times of the battery, and the expression is as follows:

[0072] N=f2(P SCr ,Q SCr ,P Batr ,Q Batr ) (2)

[0073] In order to record the cycle number of the battery, the charge and discharge state quantity state(t) is introduced; by calculating the change number of the value of the charge and discharge state quantity state(t), the charge and discharge conversion times of the battery are obtained; the duration of the charge and discharge state quantity state(t) is calculated, and the charge and discharge time of the battery is obtained.

[0074]

[0075] In the above formula, P SCr , P Batr are the rated power of the super capacitor and the battery respectively; Q SCr , Q Batr are the rated capacity of the super capacitor and the battery respectively; C PSC , C PBat are the rated power unit prices of the super capacitor and the battery respectively; C QSC , C QBat are the rated capacity unit prices of the super capacitor and the battery respectively.

[0076] Based on the objective functions established above regarding the cost of the hybrid energy storage system and the number of charge-discharge conversions of the battery, this invention optimizes the capacity configuration of the hybrid energy storage device with the goal of minimizing each objective function. However, the objective functions are contradictory; improving one objective function requires sacrificing the performance of another. Therefore, the mathematical model of the hybrid energy storage system is a multi-objective optimization problem.

[0077] Based on the above analysis, this invention takes the minimum cost of the hybrid energy storage system and the minimum number of charge-discharge conversions of the battery as the objective functions, and uses the rated power and rated capacity of the supercapacitor and the rated power and rated capacity of the battery as decision variables to establish a multi-objective optimization mathematical model for the hybrid energy storage system, as shown in the following equation:

[0078]

[0079] To quantitatively describe the optimization effect of applying hybrid energy storage systems to mitigate wind power fluctuations, this invention defines the state-of-charge coefficient h1 and grid connection smoothing rate h2 of the hybrid energy storage system as evaluation indicators for multi-objective optimization of the hybrid energy storage system. Let the time during which the hybrid energy storage system participates in mitigating wind power fluctuations be [t1, t2]. n The sampling time interval is 1 second.

[0080] (1) Definition 1: State of charge coefficient h1 of hybrid energy storage system:

[0081]

[0082] This coefficient is obtained by measuring the real-time state of charge (SOC) of the supercapacitor and battery during operation. SC Deviation from the median value of the state of charge The sum of distances and the maximum and minimum states of charge The ratio of the sum of the differences is used to characterize the ability of a hybrid energy storage system to mitigate wind power fluctuations. If the state of charge level of the hybrid energy storage system is mostly in the middle state during the mitigation process, then the hybrid energy storage system can absorb and release more energy, has sufficient available capacity to mitigate wind power fluctuations, and has a strong ability to mitigate wind power fluctuations. The smaller the h1 value, the more points the hybrid energy storage system has in the operation with a state of charge close to the middle position, and the stronger its ability to mitigate wind power fluctuations.

[0083] (2) Define the grid connection smoothing rate h2:

[0084]

[0085] Using the grid connection smoothing rate h2 as the evaluation index, its value is the power surplus ΔP that the hybrid energy storage system cannot fully absorb. Hybrid The absolute value variance and the grid-connected power P of wind powerGrid The ratio of the absolute value variance, the coefficient is used to represent the mixed energy storage system to the wind power fluctuation power suppression effect; the smaller the h2 value, the more the power remaining ΔP that the mixed energy storage system cannot completely absorb Hybrid The smaller the power incorporated into the grid, the less the wind power fluctuation power smoothing effect is better.

[0086] In the established mathematical model of the hybrid energy storage system, when configuring the capacity of the hybrid energy storage system, the state of charge, maximum power, and power instantaneous balance of each energy storage element should be considered, such as the wind power grid-connected power should meet the grid-connected requirements; considering the influence of deep charge and discharge on the energy storage medium, the state of charge change range of the energy storage medium needs to be set; the charge and discharge power and capacity of the energy storage medium must be limited within the rated range to ensure the effectiveness of the capacity configuration of the hybrid energy storage system. The constraint condition in the capacity optimization configuration process of the hybrid energy storage system is as follows:

[0087]

[0088] In the formula: ΔP Grid,1min (t) is the 1min wind power grid-connected power fluctuation at time t; the 30min wind power grid-connected power fluctuation at time t; ΔP Grid,30min (t)γ 1min is the maximum value of 1min wind power grid-connected power fluctuation; γ 30min is the maximum value of 30min wind power grid-connected power fluctuation; Q Bat is the battery capacity; Q SC is the super capacitor capacity; Q Bat.r is the rated battery capacity; Q SC.r is the rated super capacitor capacity.

[0089] In the multi-objective mathematical model of the hybrid energy storage system, there is a mutual conflict between the minimum cost function and the minimum battery charge and discharge frequency function, which is consumed and eliminated. The two objectives of the minimum cost function and the minimum battery charge and discharge frequency function are a multi-objective optimization problem, and the value cannot be optimized at the same time. Only through coordination and compromise can the Pareto optimal solution be achieved, which is the optimal solution of both objective functions. Compared with single-objective function optimization, multi-objective optimization needs to optimize multiple objective functions at the same time, greatly increasing the complexity of optimization. The commonly used multi-objective optimization algorithms at present mainly include traditional multi-objective optimization methods such as weight method, evolutionary-based multi-objective optimization algorithms such as particle swarm-based multi-objective optimization method, etc.

[0090] In order to solve the multi-objective function of the hybrid energy storage system, the multiple objectives in the model are aggregated into a function by the entropy weight method to determine the weight coefficient method; and the improved particle swarm algorithm is used to solve the single objective function of the hybrid energy storage system with the constraint condition, so that the optimal solution of the capacity of the hybrid energy storage system is obtained.

[0091] The application firstly gives a certain weight λ to each sub-objective function i Then, the weighted sub-objective functions are added, so that the weighted multi-objective optimization problem with the constraint condition is converted into a single objective function optimization problem with the positive coefficient and the constraint condition, wherein the constraint condition is set as the set X, that is, the constraint condition of the capacity optimization configuration, and then the optimization is carried out on the constraint set X to obtain the optimal solution of the function.

[0092] The multi-objective function of the hybrid energy storage system in the application is aggregated into a single objective function as follows:

[0093]

[0094] The weight coefficient λ of each sub-objective function in the objective function i reflects the relative importance of the objective function in the overall evaluation; the larger the weight coefficient, the more important the objective function in the overall evaluation, and the smaller the weight coefficient, the smaller the influence in the overall evaluation, even zero. Generally, the weight coefficient of the sub-objective function is determined by the decision maker according to his own experience, mainly according to the importance of each objective function in the overall evaluation to set the size of the weight coefficient. However, this method is subjective and is suitable for the case where the decision maker is very familiar with and understands the evaluation object. Compared with the subjective assignment method, the entropy weight method can better explain the obtained results according to the size of the information entropy of the index and the role of the comprehensive evaluation index, and has strong objectivity. Therefore, in order to avoid the subjectivity of human beings, the entropy weight method is used to determine the weight coefficient λ of each sub-objective function in the objective function. i The specific implementation process of the entropy weight method of the application is as follows:

[0095] The entropy weight method used in the application is to determine the effective degree of information provided by the index according to the size of the index information entropy:

[0096] If the information entropy is small, the effective degree is large, and the weight value in the comprehensive evaluation index is large; on the contrary, the weight of the index is small. Therefore, the entropy weight method is to rely on the variation of different indexes, and the most weight value is obtained by calculating and correcting each index by using the information entropy.

[0097] Based on the principle of the entropy weight method, the process of determining the weight of each index by the entropy weight method is described as follows:

[0098] 1) an original matrix X=[aij ] m×n :

[0099]

[0100] 2) Data standardization

[0101] In the established index system, the original data needs to be standardized. Because the properties of each evaluation index are different, such as different dimensions, direct analysis of the original data cannot correctly reflect the comprehensive results of different forces. Therefore, the original data is processed by a fuzzy quantization mode to obtain a standardized matrix K=[b ij ] m×n ; the fuzzy quantization expression is as follows:

[0102]

[0103] In the formula: b ij is the corresponding value of the element a ij in the matrix X; a ·j is the original data of the jth index corresponding to the m evaluation objects.

[0104] The standardized matrix K=[b ij ] m×n is obtained from 2), and the proportion of the ith evaluation object in the jth evaluation index is calculated:

[0105]

[0106] The proportion matrix P=[p ij ] m×n is obtained.

[0107] 3) The entropy value i j of the jth index is calculated, and I=[i ij ] 1×n :

[0108]

[0109] Where: when p ij =0, p ij lnp ij =0.

[0110] 4) The entropy weight of the jth index is obtained as:

[0111]

[0112] The entropy weight matrix W=[w ij ] 1×n is obtained.

[0113] The weight of each index is determined through the above steps, and each index is weighted and calculated through the weight value, so that the specific object is better objectively evaluated.

[0114] The particle swarm algorithm is a simulation of the predatory behavior of birds, and is a heuristic global search optimization algorithm.

[0115] In the particle swarm algorithm, position, speed and fitness value are three important evaluation indexes of particles, which are used to represent the characteristics of particles.

[0116] x i (t+1)=x i (t)+v i (t+1)t=1,2,…,T max

[0117] v i (t+1)=ωv i (t)+c1r1(p i.best -x i (t))+c2r2(g best -x i (t))t=1,2,…,T max

[0118] In the formula, v is the speed of the particle, x is the position of the particle, p i.best is the individual extreme value, g best is the group extreme value, c1 and c2 are acceleration factors, t is the current iteration number, i is the i-th particle in the population, omega is the inertia weight, r1 and r2 are random numbers in the interval;

[0119] The individual extreme value and the group extreme value of the newly generated particle swarm are compared with the individual extreme value and the group extreme value of the last generation, and the position of the individual extreme value and the group extreme value are updated. Through continuous comparison and iteration, the best population group extreme value is obtained. The population group extreme value at this time is the optimal solution of the objective function.

[0120] In the particle swarm algorithm parameters, the role of the inertia weight constant omega is to balance the global convergence and convergence speed of the algorithm. Larger inertia weight constant omega will make the particle have a larger speed, so that the particle swarm algorithm has strong global search ability and avoids premature phenomenon; smaller inertia weight constant omega will make the particle have strong local development ability, and the algorithm converges faster. Therefore, in the particle swarm algorithm, many scholars adjust the size of the inertia weight to improve the performance of the algorithm.

[0121] In order to make the algorithm have strong global convergence and convergence speed, the present application adopts the method of dynamically adjusting the inertia weight coefficient according to the size of the particle objective function value, as shown in the following formula:

[0122]

[0123] In the formula: omega min and omega max are the minimum and maximum values of the inertia weight constant omega; f min and f avg are the minimum and average values of the current particle objective function.

[0124] Compared with the method of fixed weight or linear adjustment of weight coefficient, the dynamic weight method automatically adjusts the weight size according to the particle objective function value, maintains the diversity of the algorithm weight. When the particle objective function value is larger than the average objective function value, it means that the value of the particle objective function is far from the minimum value, so the inertia weight is reduced to improve the local development ability of the algorithm. When the particle objective function value is smaller than the average objective function value, it means that the value of the particle objective function is close to the minimum value, so the inertia weight is increased to avoid the premature of the algorithm and increase the global search ability.

[0125] The algorithm flow is as follows:

[0126] Step 1: initialization of population. In the initialization process, first set the value range of the particles in the population, and then randomly generate the particle speed and particle position in the constrained range.

[0127] Step 2: calculate the initial extreme value of the population. Calculate the fitness value of the particle according to the fitness function, record the position and fitness value of the particle and store it in p best ; record the position and fitness value of the optimal individual in the population and store it in g best .

[0128] Step 3: update the position and speed of the particle according to formula (13) and formula (14).

[0129] Step 4: update the inertia weight of the algorithm according to formula (15).

[0130] Step 5: individual extreme value and population extreme value updating in population. The newly generated particle extreme value is compared with the particle of the last generation, and the better particle is taken as the individual extreme value p best And the population g best .

[0131] Step 6: judging whether the algorithm stopping meets the requirement. If the condition is met, the optimal solution of the objective function, i.e. the population extreme value g best is output. If the condition is not met, step 3 is continuously executed.

[0132] Based on the established multi-objective mathematical model of the hybrid energy storage system, before the weight of each sub-objective function is determined by using the entropy weight method, the optimal solution of each sub-objective function is firstly obtained, then the weight coefficient is determined according to the optimal solution of each sub-objective function, and finally the multi-objective optimization is converted into single-objective optimization. In view of the prematurity of the particle swarm optimization (PSO), the particle swarm optimization is improved in the application, and the improved particle swarm optimization is applied to the optimization and solution of the positive coefficient single-objective function of the hybrid energy storage system with constraint conditions established in the application, so as to optimize and configure the capacity of the hybrid energy storage system.

[0133] Before the improved particle swarm optimization is used to solve the objective function of the hybrid energy storage system, the population range and population dimension in the improved particle swarm optimization are firstly determined. Since there are four decision variables in the objective function, the position of the particle should represent the values of the four decision variables, and the dimension N of the population is 4. The values of the four decision variables should all meet the constraint condition of capacity configuration, so the range of the population is the constraint condition of capacity configuration, and the particle must be optimized in the population range.

[0134] The optimization flowchart of the capacity of the hybrid energy storage system based on the improved particle swarm optimization designed in the application is shown in Figure 1 .

[0135] The specific implementation process of the algorithm is as follows:

[0136] 1) Population initialization. The appropriate population particle size is set, and the particle speed v(0)=[v1,v2,v3,v4] T and the particle position x(0)=[x1,x2,x3,x4] T in the population are randomly initialized; the particle position in the population represents the numerical values of the four decision variables under the constraint condition, i.e. the numerical values of the four decision variables of the rated power of the super capacitor and the battery and the rated capacity of the super capacitor and the battery; wherein the values of the four decision variables should all meet the constraint condition of capacity configuration.

[0137] 2) Calculate the initial extreme value of the population. The initial position x(0)=[x1,x2,x3,x4] TSubstitute the built hybrid energy storage system model, run simulation; if the constraints during the process of smoothing, such as grid-connected fluctuation conditions, the particle is invalid, and the particle needs to be regenerated and run simulation again until the particle meets the capacity configuration constraint condition. Then, the initial individual extreme value p best and the group extreme value g best are obtained according to the hybrid energy storage system single objective function.

[0138] 3) Update the position and velocity of the particle. The particle is updated according to the following formula, and the newly generated particle also needs to meet the capacity configuration constraint condition of the particle. For the particle that does not meet the condition, it needs to be regenerated.

[0139] x i (t+1)=x i (t)+v i (t+1)t=1,2,…,T max

[0140] v i (t+1)=ωv i (t)+c1r1(p i.best -x i (t))+c2r2(g best -x i (t))t=1,2,…,T max

[0141] 4) Update the inertia weight of the algorithm according to formula (15).

[0142] 5) Update the individual extreme value and the group extreme value in the population. Substitute the position x(t+1)=[x1,x2,x3,x4] T of the newly generated particle into the built hybrid energy storage system model, run simulation, and the particle that does not meet the constraint condition needs to be regenerated. Then, the individual extreme value p best and the group extreme value g best are obtained according to the hybrid energy storage system single objective function, and are compared with the individual extreme value and the group extreme value of the particle in the last generation. If the individual extreme value p best and the group extreme value g best of the newly generated particle are better than those of the particle in the last generation, the individual extreme value and the group extreme value of the newly generated particle are taken as the current individual extreme value and the current group extreme value; otherwise, the individual extreme value and the group extreme value of the particle in the last generation are taken as the current individual extreme value and the current group extreme value.

[0143] 6) According to the evolution generation or the accuracy algorithm setting condition, judge whether the algorithm stops or continues to evolve. If the condition meets the requirement, the search is ended, and the optimal solution of the objective function, i.e. the group extreme value g best is output. If the condition does not meet the requirement, return to continue step 3.

[0144] The larger the capacity of the hybrid energy storage system selected is, the better the effect of wind power output smoothing is. However, the larger the capacity of the hybrid energy storage selected is, the higher the design cost is, which leads to a significant reduction in the cost performance, and even affects the economic benefits of the wind power plant. Therefore, the appropriate capacity of the hybrid energy storage system is related to its application in actual engineering.

[0145] The present application draws the following conclusions:

[0146] (1) Considering the economy of the hybrid energy storage system, the present application establishes a cost of the hybrid energy storage system as a target function; in order to improve the service life of the system, the present application establishes a target function of the number of charge-discharge conversion of the battery for limiting the number of charge-discharge conversion of the battery; therefore, the present application takes the cost of the hybrid energy storage system and the number of charge-discharge conversion of the battery as the target function, takes the rated power and the rated capacity of the super capacitor and the rated power and the rated capacity of the battery as the decision variable, and takes the technical index as the constraint condition, to establish a multi-objective function model of the hybrid energy storage system with the constraint condition.

[0147] (2) The present application defines the state of charge coefficient h1 and the grid-connected smoothing rate h2 of the hybrid energy storage system as two evaluation indexes of the multi-objective optimization of the hybrid energy storage system, which are used to measure the smoothing effect of the wind power fluctuation power of the hybrid energy storage system with different energy storage modes and control strategies after the capacity optimization configuration. Considering the self-limitation of the energy storage element and the requirements of the wind power grid connection, the constraint conditions of the capacity optimization configuration are formulated.

[0148] (3) The optimization of the multi-objective function is much more complex than that of the single objective function, and the multi-objective optimization is converted into the single objective optimization through the weighting method. The entropy weight method is used to determine the size of the weight coefficient, and the importance of the sub-objective function is objectively evaluated. Finally, the improved particle swarm algorithm is used to solve the single objective function with the weight coefficient, to obtain the optimal solution of the capacity of the hybrid energy storage system.

[0149] The above is the preferred embodiment of the present application, and any change made according to the technical solution of the present application, as long as the function generated does not exceed the scope of the technical solution of the present application, belongs to the protection scope of the present application.

Claims

1. A hybrid energy storage system capacity optimization method, characterized in that, Firstly, a multi-objective mathematical model is established to minimize the cost of the hybrid energy storage system and the number of charge-discharge conversion of the battery; Then, the multi-objective optimization is converted into a single-objective optimization problem with weights by using the entropy weight method, and the improved particle swarm algorithm is used to optimize the single-objective function with weights to obtain the optimal solution of the power and capacity of the hybrid energy storage system, which is implemented as follows: 1) initialization of the population; setting the population particle size, randomly initializing the particle velocity v(0) = [v1, v2, v3, v4] in the population T and the particle position x(0) = [x1, x2, x3, x4] T ; the particle position in the population represents the values of the four decision variables under the condition of meeting the constraints, that is, the values of the four decision variables of the rated power of the super capacitor and the battery and the rated capacity of the super capacitor and the battery; 2) Calculate the initial extreme value of the population; the initial position of the particle x(0) = [x1, x2, x3, x4] T Substitute the built hybrid energy storage system model, run simulation; if the process of flattening, can not be constrained conditions, the particle is invalid, need to generate particles, run simulation again, until the particle meets the capacity configuration constraints; Then according to the single-objective function of the hybrid energy storage system, the initial individual extreme value p best and the group extreme value g best are obtained. 3) Update the position and velocity of the particle; update the particle according to the following formula, and the newly generated particle also needs to meet the constraint condition of the particle capacity configuration. For the particles that do not meet the condition, they need to be regenerated; x i (t+1) = x i (t) + v i (t+1) t = 1,2,...,T max v i (t+1) = ωv i (t) + c1r1(p i.best -x i (t) + c2r2(g best -x i (t) t = 1, 2,..., T max v is the velocity of the particle, x is the position of the particle, p i.best is the individual extremum, g best is the group extremum, c1 and c2 are acceleration factors, t is the current iteration number, i is the i-th particle in the population, ω is the inertia weight, r1 and r2 are random numbers generated in the interval; 4) Update the inertia weight of the algorithm according to the following formula: ω min and ω max are the minimum and maximum values of the inertia weight constant ω, respectively; min and f avg are the minimum and average values of the current particle objective function, respectively; 5) Population individual extreme value and population extreme value update: the position of the updated particle x(t+1)=[x1,x2,x3,x4] T is substituted into the built hybrid energy storage system model, and simulation is run; similarly, particles that do not meet the constraint condition need to be regenerated; the individual extreme value p best and the population extreme value g best are obtained, and they are compared with the individual extreme value and the population extreme value of the last generation of particles; if the individual extreme value p best and the population extreme value g best of the newly generated particle are better than those of the last generation, the particle is taken as the current individual extreme value and population extreme value; On the contrary, the individual extreme value and the group extreme value of the last generation of particles are kept as the current individual extreme value and the group extreme value; 6) According to the evolution number or the accuracy algorithm setting condition, judge whether the algorithm stops or continues to evolve; If the condition is satisfied, the search ends and the optimal solution of the objective function, i.e. the group extremum g, is output best If the condition is not satisfied, the step 3) is returned to continue to be executed. Finally, the state of charge coefficient of the hybrid energy storage system and the grid-connected smoothing rate are defined as the evaluation indexes of the multi-objective optimization of the hybrid energy storage system, which is implemented as follows: The time for participating in the mixed energy storage system to suppress wind power fluctuation is [t1, t n ] and the sampling time interval is 1s; (1) Define the state of charge coefficient h1 of the hybrid energy storage system: The state of charge coefficient h1 of the hybrid energy storage system is the ratio of the sum of the differences between the real-time state of charge SOC of the super capacitor and the battery during the operation time and the middle value SOC medu of the state of charge, and the sum of the differences between the maximum SOC max and the minimum SOC min of the state of charge. The smoothing capability of the hybrid energy storage system for the fluctuating power of the wind power is represented by the ratio of the sum of the differences between the sum of the distances and the maximum SOC max of the state of charge, and the sum of the differences between the minimum SOC min of the state of charge. (2) Define the grid-connected smoothing rate h2: P Wind , P SC , P Bat are the powers of wind power, super capacitor and battery, respectively, h2 is the grid-connection smoothing rate, and the value of h2 is the absolute value variance of the power surplus ΔP Hybrid that the hybrid energy storage system cannot completely absorb, i.e., var|ΔP Hybrid | divided by the absolute value variance of the wind power P Grid , var|P Grid |, and the grid-connection smoothing rate h2 is used to represent the suppression effect of the hybrid energy storage system on the fluctuating power of wind power; the smaller the value of h2, the smaller the power surplus ΔP Hybrid that the hybrid energy storage system cannot completely absorb, and the less the power connected to the grid, and the better the smoothing effect on the fluctuating power of wind power.

2. The method of claim 1, wherein, The specific implementation of the multi-objective mathematical model established to minimize the cost of the hybrid energy storage system and the number of charge-discharge conversion of the battery is as follows: 1.1) Establish the cost of the hybrid energy storage system as one of the objective functions, and the rated power of the super capacitor and the rated capacity of the super capacitor and the battery as the decision variables, which is expressed as 1.2) Battery charge-discharge conversion number objective The number of cycles of the battery is used as another objective function of the hybrid energy storage system to constrain the number of charge-discharge conversion of the battery, which is expressed as follows: N = f2(P SCr Q SCr P Batr Q Batr ) (2) In order to record the number of cycles of the battery, the charge-discharge state quantity state(t) is introduced; by calculating the number of changes of the value of the charge-discharge state quantity state(t), the number of charge-discharge conversion of the battery is obtained; by calculating the duration of the charge-discharge state quantity state(t), the time of one charge-discharge of the battery is obtained; P, P SCr , P Batr are the rated power of the supercapacitor, of the battery, respectively; Q SCr , Q Batr are the rated capacity of the supercapacitor, of the battery, respectively; C PSC , C PBat are the unit price of the rated power of the supercapacitor and of the battery, respectively; C QSC , C QBat are the unit price of the rated capacity of the supercapacitor and of the battery, respectively; 1.3) Based on the established objective functions of the cost of the hybrid energy storage system and the number of charge-discharge conversion of the battery, the capacity of the hybrid energy storage device is optimized and configured to minimize the objective functions, and the rated power and rated capacity of the super capacitor and the rated power and rated capacity of the battery are used as the decision variables to establish a multi-objective optimization mathematical model of the hybrid energy storage system, which is shown in the following formula: f1 and f2 are the objective functions of the cost of the hybrid energy storage system and the number of charge-discharge conversion of the battery, respectively.

3. The method of claim 1, wherein, The specific implementation of converting the multi-objective optimization into a single-objective optimization problem with weights by using the entropy weight method is as follows: First, the entropy weight method is used to determine the weight coefficient λ of each sub-objective function in the objective function i Then, the weighted sub-objective functions are added to convert the multi-objective optimization problem with constraints into a single-objective function optimization problem with positive coefficients and constraints, where the constraint condition is set as the set X, i.e. the constraint condition of capacity optimization configuration, and then the optimal solution of the function is obtained by optimizing the function on the constraint set X; the multi-objective function of the hybrid energy storage system is aggregated into a single-objective function as follows: f1, f2 are two sub-objective functions in the target function, and λ is a weight coefficient of each sub-objective function in the target function i , reflecting the relative importance of the corresponding sub-objective function in the overall evaluation.

4. The method of claim 3, wherein, The weight coefficient λ of each sub-target function in the target function is determined by using an entropy weight method i , and specifically as follows: 2.1) Establishing the original matrix X = [a ij ] m×n : 2.2) Data standardization In the established index system, the original data is processed by using fuzzy quantification mode to obtain the standardization matrix K=[b ij ] m×n ; the fuzzy quantification expression is as follows: wherein: b ij is the element a ij the corresponding value after standardization; a ·j is the original data of the index j corresponding to m evaluation objects; From 2.2) the normalized matrix K = [b ij ] m×n , the proportion of the i-th evaluation object in the j-th evaluation index is calculated: The proportionality matrix P = [p ij ] m×n ; 2.3) Calculate the entropy value i of the jth indicator j , giving I = [i ij ] 1×n : where: let p ij = 0, p ij ln p ij = 0; 2.4) The entropy weight of the jth index is obtained as follows: The entropy weight matrix W = [w ij ] 1×n .

5. The method of claim 1, wherein, The constraint condition in the capacity optimization configuration process of the hybrid energy storage system is as follows: In the formula: P Hybrid (t), P Wind (t), P SC (t), P Bat (t), P Grid (t) are respectively the power of the hybrid energy storage system, the wind power, the super capacitor, the battery, and the grid-connected wind power at time t, SOC Bat (t), SOC (t), SOC SC (t) are respectively the minimum charge value of the battery, the maximum charge value of the battery, the charge value of the battery at time t, the minimum charge value of the super capacitor, the maximum charge value of the super capacitor, and the charge value of the super capacitor at time t, ΔP Grid,1min (t) is the 1-minute grid-connected wind power fluctuation at time t; ΔP Grid,30min (t) is the 30-minute grid-connected wind power fluctuation at time t; γ 1min is the maximum value of the 1-minute grid-connected wind power fluctuation; γ 30min is the maximum value of the 30-minute grid-connected wind power fluctuation; Q Bat is the capacity of the battery; Q SC is the capacity of the super capacitor; Q Bat.r is the rated capacity of the battery; Q SC.r is the rated capacity of the super capacitor.

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