A method for optimizing energy storage system capacity configuration taking into account battery module capacity

Abstract

The present invention relates to a battery energy storage system, and specifically to a method for optimizing the capacity configuration of an energy storage system taking into account the capacity of a battery module. The optimization method mainly includes the following steps: first, obtaining typical daily load power consumption and new energy power generation scenarios; then, establishing a battery energy storage system operation behavior model and a full life cycle economic model including objective functions and constraints; then, taking the battery energy storage system capacity configuration economic optimization as the outer layer and the battery energy storage system operation behavior optimization as the inner layer as the upper layer optimization as the framework, a hybrid frog leaping swarm intelligent algorithm for integer programming is proposed to iteratively optimize the configuration scheme of the battery energy storage system; finally, a final configuration scheme of the battery energy storage system is formed. The present invention can avoid the disadvantage that the optimized battery system is difficult to integrate the minimum battery unit, and while taking into account the economy and source-grid-load-storage requirements, it provides a more reasonable planning for the practical application of the battery energy storage system.

Description

Technical field:

[0001] The present invention relates to a battery energy storage system, and in particular to a method for optimizing the capacity configuration of an energy storage system taking into account the capacity of a battery module. Background technology:

[0002] With the high penetration of new energy in the power grid, the problem of effective guarantee of power grid stability and safety and power performance has become increasingly prominent. Proper configuration of energy storage systems on the new energy side, in the power grid, and at the user end will help reduce the impact of new energy on the stability of the power grid, improve the power quality of the power grid, and the flexibility of user access. Therefore, electrochemical energy storage systems have made great progress in recent years.

[0003] From the perspective of the construction and use of energy storage systems, the system needs top-level planning and layout to serve new energy power sources, power grids and load users more effectively, reasonably and economically, and avoid the waste of power resources. At present, the planning of electrochemical energy storage systems is roughly centered around three issues: battery selection, battery system site selection and battery system capacity configuration. Among them, battery selection focuses on whether the battery performance can meet the matching problem of application requirements, and the site selection of energy storage batteries is mainly manifested in the engineering construction problem that depends on the scale of the power grid, the influence of relevant parameters, service requirements and field feasibility. Energy storage capacity configuration is mainly an optimization problem based on the application scenario of energy storage batteries and economic orientation. Experts from different directions have proposed many methods from multiple angles for these three types of problems and achieved phased results. In terms of energy storage battery capacity configuration, most of the existing methods are based on the theoretical calculation process of "application behavior modeling-economic behavior modeling-operation control simulation-optimization solution containing energy storage systems" as the technical route. The equivalence of the application behavior model, economic model and operation control simulation process determines the rationality of capacity configuration. A large number of experts and scholars have carried out extensive research on this. However, during the optimization process, the capacity and power of energy storage batteries are often defined as continuous variables for optimization design, while ignoring the limitation of the minimum module unit in the integration process, that is, the power and capacity of the energy storage system are represented as positive integer multiples of the minimum battery module unit. This neglect will make the planned capacity difficult to implement and integrate, and only approximate values ​​can be used for design, which cannot guarantee that the energy storage system is the best configuration solution. Summary of the invention:

[0004] Based on this, the present invention changes the iterative process of the hybrid frog leaping group intelligent optimization algorithm to realize integer programming of positive integer multiples of the minimum unit of the energy storage battery, thereby forming an energy storage system capacity configuration method that is conducive to the integration of energy storage batteries, which has practical significance for the realization and application of the energy storage system. The specific technical solution is as follows:

[0005] A method for optimizing capacity configuration of an energy storage system taking into account the capacity of a battery module, comprising:

[0006] Step 1: Collect the local renewable energy and load historical data involved in the system, and obtain the renewable energy daily power curve and load daily power curve under multiple typical scenarios;

[0007] Step 2: According to the operation process and economic parameters of battery energy storage, construct the operation economic model and behavior model of the battery energy storage system; specifically, the following steps are included:

[0008] Step 2.1: The full life cycle economic model of the battery energy storage system includes: the construction cost, operation and maintenance cost, recycling value and operation service income of the battery energy storage system, which is obtained through the following formula:

[0009]

[0010] Where c1, c2, c3, and c4 represent the unit power construction cost, unit capacity construction cost, unit power maintenance cost, and unit power recovery value of the battery energy storage system, respectively. e and E e The rated power and rated capacity of the smallest unit of the energy storage battery to be used in the energy storage system to be built, R represents the operating life of the battery energy storage system, β r represents the cash value factor in the rth year, x represents the minimum number of units for battery energy storage system integration, which is a positive integer, J2(P B (t)) indicates that the output of the energy storage battery at time t is P B (t) operating service income;

[0011] J2(P B (t)) is obtained by the following formula,

[0012]

[0013] Among them, C earn (t) represents the service income per unit power of the energy storage battery at time t;

[0014] The operating life R of the battery energy storage system is obtained by the following formula:

[0015]

[0016] Among them, Loss(t) represents the cycle life loss of the battery energy storage system at time t, which is obtained by the following formula:

[0017]

[0018] Where t = 1, 2, ..., T, T represents the number of data collection points on a typical day, Ncyc (t) represents the cycle life of the battery energy storage system at time t, which is obtained by the following formula. This functional relationship can be obtained by fitting or regression method based on the cycle life test data of the selected battery when it leaves the factory.

[0019] N cyc (t) = f(S SOE (t))

[0020] Among them, S SOE (t) represents the energy state of the energy storage battery,

[0021] Cash value factor β in year r r It can be obtained by the following formula:

[0022]

[0023] Where ξ represents the discount rate,

[0024] Step 2.2: Build a battery energy storage system behavior model:

[0025]

[0026] Among them, P B (t) is the active power output of the energy storage battery at the tth moment, E = xE e represents the capacity of the battery energy storage system, x represents the number of the smallest battery unit integrated in the energy storage system, S SOE (t) represents the capacity status of the battery energy storage system at time t, Δt represents the sampling time interval, η ch and η dis Indicates the charging and discharging efficiency of the battery energy storage system, which can be obtained from the performance parameters of the battery when it leaves the factory or calculated by actual measurement;

[0027] In addition to satisfying the operating characteristics of the energy storage system in the above formula, the battery energy storage system behavior model also needs to define some constraints based on the characteristics of the power grid and the energy storage system itself.

[0028] System power balance constraints:

[0029] Among them, P Grid (t), P Source (t), P Load (t) and P B (t) represents the power of the power grid, the power of new energy, the power of the load and the power of the battery at time t respectively; and the following constraints are met:

[0030] The active power fluctuation range of the power grid needs to be within the specified value δ:

[0031]

[0032] The output power of the battery energy storage system at any moment must be limited to the rated power of the energy storage system:

[0033]

[0034] The energy state of the battery energy storage system should be kept at the lower limit at every moment and upper limit Within:

[0035]

[0036] Energy balance of battery energy storage system in a day: S SOE (1) = S SOE (T),

[0037] The maximum range of battery energy storage system output change rate: Among them, λ represents the maximum multiple parameter of rated power,

[0038] Battery energy storage system during integration for DC model voltage range: U - ≤yU e ≤U + , where y represents the number of battery minimum units connected in series, which is a positive integer, and U - and U + Respectively represent the upper and lower limits of DC voltage;

[0039] During the integration of the battery energy storage system, the total number of the smallest battery units must be an integer multiple of their series connection: mod(x,y)=0, where mod(·) represents the function of finding the remainder. represents a positive integer;

[0040] Step 3: Using the frog jumping step algorithm, the battery energy storage system capacity configuration is used as the outer layer of the frog jumping step algorithm, and the operating condition simulation is used as the inner layer of the frog jumping step algorithm to construct a two-layer optimized battery energy storage system capacity configuration plan. The process includes the following steps:

[0041] Step 3.1: Select the power curve of the typical daily scenario of the k1th renewable energy and the k2th load obtained in step 1, k1=1,2,…,K1, k2=1,2,…,K2, and define the number of frogs in the outer population as n w1 , the number of outer sub-populations is n w2 , the maximum number of iterations WiterMax, randomly initialize the position of each frog in the outer optimization in the positive integer range, where the position of the i-th frog X i =[x i ,y i] represents the minimum number of modules and the number of series connections of the battery energy storage system, i = 1, 2, ..., n w1 , and set Witer=1, i=1, and enter the inner layer operation condition simulation optimization, i.e. step 3.2;

[0042] Step 3.2: Use the energy storage battery output in step 2.1 to operate the service income J2 (P B (t)) is the optimal target, and the traditional hybrid frog leaping algorithm is used to calculate n w1 The advantages and disadvantages of the frog positions are sorted, and the inner frog positions are updated to iterate the optimal battery energy storage output curve;

[0043] Step 3.3: Based on the output of the energy storage system represented by the optimal frog in the inner layer at the i-th frog position in the outer layer in step 3.2, calculate the objective function value J1(i) when the outer layer is at the i-th frog position;

[0044] Step 3.4: Determine whether i is less than or equal to n w1 ; If yes, set i=i+1 and return to step 3.2; if no, proceed to step 3.5;

[0045] Step 3.5: According to the outer layer n w1 The function values ​​of the frog positions {J1(1),J1(2),…,J1(n w1 )} size, and then distribute to n w2 subpopulations, and update the outer n w1 The position of the frogs;

[0046] Step 3.6: Determine whether Witer is less than or equal to WiterMax; if so, set Witer = Witer + 1, i = 1 and return to step 3.2.2; if not, proceed to step 3.7;

[0047] Step 3.7: Based on the results of step 3.6, the outer optimization result can be obtained, that is, in the typical day scenario of the k1th new energy and the k2th load, the battery energy storage system needs to be configured with x Best (k1, k2) minimum battery cells and y Best (k1, k2) minimum battery cells are connected in series;

[0048] Step 4: Calculate the battery energy storage system configuration plan for the typical daily scenario of K1 new energy sources and K2 loads in step 1 using the method in step 3 and the model in step 2, and form a final capacity configuration plan; specifically, the following steps are included:

[0049] Step 4.1: Let the values ​​of k1 and k2 in step 3 traverse all scenarios of K1 new energy sources and K2 loads, and calculate the battery energy storage system configuration plan under each scenario, that is, the solution set H = {(x Best(k1,k2),y Best (k1,k2))|k1=1,2,…,K1,k2=1,2,…,K2};

[0050] Step 4.2: The configuration scheme with the most minimum battery units in the battery energy storage system of scheme set H is recorded as the most conservative configuration scheme of the battery energy storage system, and the configuration scheme with the least minimum battery units in the battery energy storage system of scheme set H is recorded as the most aggressive configuration scheme of the battery energy storage system, completing the final capacity configuration of the battery energy storage system.

[0051] Compared with the closest prior art, the excellent effects of the present invention are:

[0052] In the technical solution of the present invention, from the perspective of practical engineering application, the minimum energy storage battery unit exists in the integration process of the energy storage battery, that is, the optimal configuration of the energy storage capacity is expressed as the solution of a discrete, positive integer programming problem of a positive integer multiple of the minimum unit. The existing swarm intelligence optimization method is difficult to form an optimization of integer programming, and the classical traditional method is prone to local optimality and difficult to deal with complex nonlinear constraints. The present invention changes the update iteration process of the frog position in the traditional hybrid frog leaping algorithm, first pre-updates the position range in a non-integer manner, and then uses a random integer step size to achieve integer updates within the pre-update range, thereby realizing the integer programming problem of the inherited swarm intelligence optimization algorithm. It is suitable for the capacity configuration problem of the energy storage system under the integration of the minimum unit of the energy storage battery. The present invention improves the practicality of the planning results of the energy storage power station, which will be beneficial to the subsequent integrated application of the battery energy storage power station. Description of the drawings:

[0053] The present invention is further described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a flow chart of the energy storage system capacity configuration optimization method of the present invention.

[0055] Figure 2 It is a method flow chart of step 3 of the present invention.

[0056] Figure 3 It is a method flow chart of step 3.2 in an embodiment of the present invention. Specific implementation method:

[0057] The following will be combined with the accompanying drawings in the embodiments of this application.

[0058] A method for optimizing capacity configuration of an energy storage system taking into account the capacity of a battery module, comprising:

[0059] Step 1: Collect the local renewable energy and load historical data involved in the system, and obtain the renewable energy daily power curve and load daily power curve under multiple typical scenarios; specifically, the following steps are included:

[0060] Step 1.1: Measure the daily active power data of local renewable energy sources Pi Source (t), where i = 1, 2, ..., Num represents the number of days of renewable energy power generation in history, t = 1, 2, ..., T represents the time of each day, based on the Euclidean distance between daily active power generation Characterize the similarity between the renewable energy power generation on the i-th day and the j-th day, and use the clustering method to construct the active power curve of K1 renewable energy typical daily power generation scenarios;

[0061] Step 1.2: Obtain the daily active power data Pi of the local load history Load (t), where i = 1, 2, ..., Num represents the number of days of historical load power consumption, t = 1, 2, ..., T represents the time of each day, based on the Euclidean distance between the daily load power consumption Characterize the similarity between the load power consumption on the i-th day and the j-th day, and use the clustering method to construct the active power curves of K2 typical daily load power consumption scenarios

[0062] Step 2: According to the operation process and economic parameters of battery energy storage, construct the operation economic model and behavior model of the battery energy storage system; specifically, the following steps are included:

[0063] Step 2.1: The full life cycle economic model of the battery energy storage system includes: the construction cost, operation and maintenance cost, recycling value and operation service income of the battery energy storage system, which is obtained through the following formula:

[0064]

[0065] Where c1, c2, c3, and c4 represent the unit power construction cost, unit capacity construction cost, unit power maintenance cost, and unit power recovery value of the battery energy storage system, respectively. e and E e The rated power and rated capacity of the smallest unit of the energy storage battery to be used in the energy storage system to be built, R represents the operating life of the battery energy storage system, β r represents the cash value factor in the rth year, x represents the minimum number of units for battery energy storage system integration, which is a positive integer, J2(P B (t)) indicates that the output of the energy storage battery at time t is P B (t) operating service income;

[0066] J2(P B (t)) is obtained by the following formula,

[0067]

[0068] Among them, Cearn (t) represents the service income per unit power of the energy storage battery at time t;

[0069] The operating life R of the battery energy storage system is obtained by the following formula:

[0070]

[0071] Among them, Loss(t) represents the cycle life loss of the battery energy storage system at time t, which is obtained by the following formula:

[0072]

[0073] Where t = 1, 2, ..., T, T represents the number of data collection points on a typical day, N cyc (t) represents the cycle life of the battery energy storage system at time t, which is obtained by the following formula. This functional relationship can be obtained by fitting or regression method based on the cycle life test data of the selected battery when it leaves the factory.

[0074] N cyc (t) = f(S SOE (t))

[0075] Among them, S SOE (t) represents the energy state of the energy storage battery,

[0076] Cash value factor β in year r r It can be obtained by the following formula:

[0077]

[0078] Where ξ represents the discount rate,

[0079] Step 2.2: Build a battery energy storage system behavior model:

[0080]

[0081] Among them, P B (t) is the active power output of the energy storage battery at the tth moment, E = xE e represents the capacity of the battery energy storage system, x represents the number of the smallest battery unit integrated in the energy storage system, S SOE (t) represents the capacity status of the battery energy storage system at time t, Δt represents the sampling time interval, η ch and η dis Indicates the charging and discharging efficiency of the battery energy storage system, which can be obtained from the performance parameters of the battery when it leaves the factory or calculated by actual measurement;

[0082] In addition to satisfying the operating characteristics of the energy storage system in the above formula, the battery energy storage system behavior model also needs to define some constraints based on the characteristics of the power grid and the energy storage system itself.

[0083] System power balance constraints:

[0084] Among them, P Grid (t), P Source (t), P Load (t) and P B (t) represents the power of the power grid, the power of new energy, the power of the load and the power of the battery at time t respectively; and the following constraints are met:

[0085] The active power fluctuation range of the power grid needs to be within the specified value δ:

[0086]

[0087] The output power of the battery energy storage system at any moment must be limited to the rated power of the energy storage system:

[0088]

[0089] The energy state of the battery energy storage system should be kept at the lower limit at every moment and upper limit Within:

[0090]

[0091] Energy balance of battery energy storage system in a day: S SOE (1) = S SOE (T),

[0092] The maximum range of battery energy storage system output change rate: Among them, λ represents the maximum multiple parameter of rated power,

[0093] Battery energy storage system during integration for DC model voltage range: U - ≤yU e ≤U + , where y represents the number of battery minimum units connected in series, which is a positive integer, and U - and U + Respectively represent the upper and lower limits of DC voltage;

[0094] During the integration of the battery energy storage system, the total number of the smallest battery units must be an integer multiple of their series connection: mod(x,y)=0, where mod(·) represents the function of finding the remainder. represents a positive integer;

[0095] Step 3: Using the frog jumping step algorithm, the battery energy storage system capacity configuration is used as the outer layer of the frog jumping step algorithm, and the operating condition simulation is used as the inner layer of the frog jumping step algorithm to construct a two-layer optimized battery energy storage system capacity configuration plan. The process includes the following steps:

[0096] Step 3.1: Select the power curve of the typical daily scenario of the k1th renewable energy and the k2th load obtained in step 1, k1=1,2,…,K1, k2=1,2,…,K2, and define the number of frogs in the outer population as n w1 , the number of outer sub-populations is n w2 , the maximum number of iterations WiterMax, randomly initialize the position of each frog in the outer optimization in the positive integer range, where the position of the i-th frog X i =[x i ,y i ] represents the minimum number of modules and the number of series connections of the battery energy storage system, i = 1, 2, ..., n w1 , and set Witer=1, i=1, and enter the inner layer operation condition simulation optimization, i.e. step 3.2;

[0097] Step 3.2: Use the energy storage battery output in step 2.1 to operate the service income J2 (P B (t)) is the optimal target, and the traditional hybrid frog leaping algorithm is used to calculate n w1 The advantages and disadvantages of the frog positions are sorted, and the inner frog positions are updated to iterate the optimal battery energy storage output curve; the specific process includes the following:

[0098] Step 3.2.1: Define the number of frogs in the inner population as n n1 , the number of inner subpopulations is n n2 , the maximum number of iterations NiterMax, randomly initialize the position of each frog in the inner optimization Z = [P B (1),P B (2),…,P B (T)], where the position of the jth frog is Z j =[P B,j (1),P B,j (2),…,P B,j (T)] represents the output power of the battery energy storage system throughout the day, j = 1, 2, ..., n n1 , the initial battery S SOE (1) = 0.5, and let Niter = 1, j = 1, and go to step 3.2.2;

[0099] Step 3.2.2: In the formula is the objective function, the formula For the operation behavior of the energy storage battery, the objective function value J2(i,j) is calculated under the following constraints when the outer layer is the i-th frog position and the inner layer is the j-th frog position;

[0100] System power balance constraints:

[0101] Among them, P Grid (t), P Source (t), P Load (t) and P B (t) represents the power of the power grid, the power of new energy, the power of the load and the power of the battery at time t respectively; and the following constraints are met:

[0102] The active power fluctuation range of the power grid needs to be within the specified value δ:

[0103]

[0104] The output power of the battery energy storage system at any moment must be limited to the rated power of the energy storage system:

[0105]

[0106] The energy state of the battery energy storage system should be kept at the lower limit at every moment and upper limit Within:

[0107]

[0108] Energy balance of battery energy storage system in a day: S SOE (1) = S SOE (T),

[0109] The maximum range of battery energy storage system output change rate: Among them, λ represents the maximum multiple parameter of rated power,

[0110] Battery energy storage system during integration for DC model voltage range: U - ≤yU e ≤U + , where y represents the number of battery minimum units connected in series, which is a positive integer, and U - and U + Respectively represent the upper and lower limits of DC voltage;

[0111] During the integration of the battery energy storage system, the total number of the smallest battery units must be an integer multiple of their series connection: mod(x,y)=0, where mod(·) represents the function of finding the remainder. represents a positive integer;

[0112] Step 3.2.3: Determine whether j is less than or equal to n n1 ; If yes, set j=j+1 and return to step 3.2.2; if no, proceed to step 3.2.4;

[0113] Step 3.2.4: According to the outer layer is the i-th frog position, the inner layer n n1 The function values ​​of the frog positions {J2(i,1),J2(i,2),…,J2(i,n n1 )} size, and then distribute to n n2 subpopulations, and update the inner n n1 The specific process includes the following steps:

[0114] Step 3.2.4.1: Sort from large to small. When the outer layer is the i-th frog position, the inner layer n n1 The function values ​​of the frog positions {J2(i,1),J2(i,2),…,J2(i,n n1 )}, for example, the frog corresponding to the maximum value is placed in the inner population 1, the frog corresponding to the second maximum value is placed in the inner population 2, and so on. n2 +1) maximum value of frogs are put back into the inner population 1 until all the inner population n n1 frogs are fully allocated to n n2 In the subpopulation, let s = 1;

[0115] Step 3.2.4.2: The worst individual in the sth subpopulation jumps to the best individual in the same population and updates its position as follows:

[0116] As shown,

[0117] Assume that the worst individual in the sth population is Z s,Worest , the optimal individual is Z s,Best , then the frog's position after the update is expected to be Z' s,Worest , if the estimated updated position Z' s,Worest J2 is greater than the original position Z s,Worest If J2 is obtained, then go to step 3.2.4.5, otherwise go to step 3.2.4.3; where rand(0,1) represents a random number in the range [0,1];

[0118] Step 3.2.4.3: The worst individual in the sth subpopulation jumps to the best individual in all inner populations to update the position, as follows: As shown,

[0119] Assume that the best individual in all populations is Z G,Best , then the frog's position after the update is expected to be Z' s,Worest, if the estimated updated position Z' s,Worest J2 is greater than the original position Z s,Worest If J2 is found, go to step 3.2.4.5, otherwise go to step 3.2.4.4;

[0120] Step 3.2.4.4: Set the worst individual Z in the sth subpopulation s,Worest Using the following formula

[0121]

[0122] Generate a random new individual Z' s,Worest , proceed to step 3.2.4.5; where rand(-1,1) represents a random number in the range [-1,1];

[0123] Step 3.2.4.5: Determine whether s is less than or equal to n n2 ; If yes, set s=s+1 and return to step 3.2.4.2; if no, proceed to step 3.2.5;

[0124] Step 3.2.5: Determine whether Niter is less than or equal to NiterMax; if so, set Niter = Niter + 1, j = 1 and return to step 3.2.2; if not, proceed to step 3.3;

[0125] Step 3.3: Based on the output of the energy storage system represented by the optimal frog in the inner layer at the i-th frog position in the outer layer in step 3.2, calculate the objective function value J1(i) when the outer layer is at the i-th frog position;

[0126] Step 3.4: Determine whether i is less than or equal to n w1 ; If yes, set i=i+1 and return to step 3.2; if no, proceed to step 3.5;

[0127] Step 3.5: According to the outer layer n w1 The function values ​​of the frog positions {J1(1),J1(2),…,J1(n w1 )} size, and then distribute to n w2 subpopulations, and update the outer n w1 The position of the frog; including the following specific processes:

[0128] Step 3.5.1: Sort the outer n from largest to smallest w1 The function values ​​of the frog positions {J1(1),J1(2),…,J1(n w1 ))}, for example, the frog corresponding to the maximum value is placed in the outer population 1, the frog corresponding to the second maximum value is placed in the outer population 2, and so on. w2+1) maximum value of frogs are put back into the outer population 1 until all the outer population n w1 The outer frogs are fully distributed to n w2 In the subpopulation, let q = 1;

[0129] Step 3.5.2: The worst individual in the qth subpopulation jumps to the best individual in the same population and updates its position, as shown in the following formula:

[0130]

[0131] Assume that the worst individual in the qth population is X q,Worest , the optimal individual is X s,Best , then the frog's position after the update is expected to be X' s,Worest , if the estimated updated position X' s,Worest J1 is greater than the original position X s,Best If J1 is obtained, then go to step 3.5.5, otherwise go to step 3.5.3; where randi(0,D1) represents a random integer in the range [0,D1];

[0132] Step 3.5.3: The worst individual in the qth subpopulation jumps to the best individual in all outer populations to update its position, as shown in the following formula:

[0133]

[0134] Assume that the best individual in all populations is X G,Best , then the frog's position after the update is expected to be X' s,Worest , if the estimated updated position X' s,Worest J1 is greater than the original position X s,Worest If J1 is found, go to step 3.5.5, otherwise go to step 3.5.4;

[0135] Step 3.5.4: Take the worst individual X in the qth subpopulation s,Worest Using the following formula:

[0136]

[0137] Generate a random new individual X' s,Worest , go to step 3.5.5; where rand(-1,1) represents a random number in the range [-1,1];

[0138] Step 3.5.5: Determine whether q is less than or equal to n w2 ; If yes, set q = q + 1 and return to step 3.5.2; if no, proceed to step 3.6

[0139] Step 3.6: Determine whether Witer is less than or equal to WiterMax; if so, set Witer = Witer + 1, i = 1 and return to step 3.2.2; if not, proceed to step 3.7;

[0140] Step 3.7: Based on the results of step 3.6, the outer optimization result can be obtained, that is, in the typical day scenario of the k1th new energy and the k2th load, the battery energy storage system needs to be configured with x Best (k1, k2) minimum battery cells and y Best (k1, k2) minimum battery cells are connected in series;

[0141] Step 4: Calculate the battery energy storage system configuration plan for the typical daily scenario of K1 new energy sources and K2 loads in step 1 using the method in step 3 and the model in step 2, and form a final capacity configuration plan; specifically, the following steps are included:

[0142] Step 4.1: Let the values ​​of k1 and k2 in step 3 traverse all scenarios of K1 new energy sources and K2 loads, and calculate the battery energy storage system configuration plan under each scenario, that is, the solution set H = {(x Best (k1,k2),y Best (k1,k2))|k1=1,2,…,K1,k2=1,2,…,K2};

[0143] Step 4.2: The configuration scheme with the most minimum battery units in the battery energy storage system of scheme set H is recorded as the most conservative configuration scheme of the battery energy storage system, and the configuration scheme with the least minimum battery units in the battery energy storage system of scheme set H is recorded as the most aggressive configuration scheme of the battery energy storage system, completing the final capacity configuration of the battery energy storage system.

Claims

1. A method for optimizing the capacity configuration of an energy storage system taking into account the capacity of a battery module, characterized in that: include: Step 1: Collect the local renewable energy and load historical data involved in the system, and obtain the renewable energy daily power curve and load daily power curve under multiple typical scenarios; Step 2: According to the operation process and economic parameters of battery energy storage, construct the operation economic model and behavior model of the battery energy storage system; specifically, the following steps are included: Step 2.1: The full life cycle economic model of the battery energy storage system includes: the construction cost, operation and maintenance cost, recycling value and operation service income of the battery energy storage system, which is obtained through the following formula: Where c1, c2, c3, and c4 represent the unit power construction cost, unit capacity construction cost, unit power maintenance cost, and unit power recovery value of the battery energy storage system, respectively. e and E e The rated power and rated capacity of the smallest unit of the energy storage battery to be used in the energy storage system to be built, R represents the operating life of the battery energy storage system, β r represents the cash value factor in the rth year, x represents the minimum number of units for battery energy storage system integration, which is a positive integer, J2(P B (t)) indicates that the output of the energy storage battery at time t is P B (t) operating service income; J2(P B (t)) is obtained by the following formula, Among them, C earn (t) represents the service income per unit power of the energy storage battery at time t; The operating life R of the battery energy storage system is obtained by the following formula: Among them, Loss(t) represents the cycle life loss of the battery energy storage system at time t, which is obtained by the following formula: Where t = 1, 2, ..., T, T represents the number of data collection points on a typical day, N cyc (t) represents the cycle life of the battery energy storage system at time t, which is obtained by the following formula. This functional relationship can be obtained by fitting or regression method based on the cycle life test data of the selected battery when it leaves the factory. N cyc (t)=f(S SOE (t)) Among them, S SOE (t) represents the energy state of the energy storage battery, Cash value factor β in year r r It can be obtained by the following formula: Where ξ represents the discount rate, Step 2.2: Build a battery energy storage system behavior model: Among them, P B (t) is the active power output of the energy storage battery at the tth moment, E = xE e represents the capacity of the battery energy storage system, x represents the number of the smallest battery unit integrated in the energy storage system, S SOE (t) represents the capacity status of the battery energy storage system at time t, Δt represents the sampling time interval, η ch and η dis Indicates the charging and discharging efficiency of the battery energy storage system, which can be obtained from the performance parameters of the battery when it leaves the factory or calculated by actual measurement; In addition to satisfying the operating characteristics of the energy storage system in the above formula, the battery energy storage system behavior model also needs to define some constraints based on the characteristics of the power grid and the energy storage system itself. System power balance constraints: Among them, P Grid (t), P Source (t), P Load (t) and P B (t) represents the power of the power grid, the power of new energy, the power of the load and the power of the battery at time t respectively; and the following constraints are met: The active power fluctuation range of the power grid needs to be within the specified value δ: The output power of the battery energy storage system at any moment must be limited to the rated power of the energy storage system: The energy state of the battery energy storage system should be kept at the lower limit at every moment and upper limit Within: Energy balance of battery energy storage system in a day: S SOE (1) = S SOE (T), The maximum range of battery energy storage system output change rate: Among them, λ represents the maximum multiple parameter of rated power, Battery energy storage system during integration for DC model voltage range: U - ≤yU e ≤U + , where y represents the number of battery minimum units connected in series, which is a positive integer, and U - and U + Respectively represent the upper and lower limits of DC voltage; During the integration of the battery energy storage system, the total number of the smallest battery units must be an integer multiple of their series connection: mod(x,y)=0, where mod(·) represents the function of finding the remainder. represents a positive integer; Step 3: Using the frog jumping step algorithm, the battery energy storage system capacity configuration is used as the outer layer of the frog jumping step algorithm, and the operating condition simulation is used as the inner layer of the frog jumping step algorithm to construct a two-layer optimized battery energy storage system capacity configuration plan. The process includes the following steps: Step 3.1: Select the power curve of the typical daily scenario of the k1th renewable energy and the k2th load obtained in step 1, k1=1,2,…,K1, k2=1,2,…,K2, and define the number of frogs in the outer population as n w1 , the number of outer sub-populations is n w2 , the maximum number of iterations WiterMax, randomly initialize the position of each frog in the outer optimization in the positive integer range, where the position of the i-th frog X i =[x i ,y i ] represents the minimum number of modules and the number of series connections of the battery energy storage system, i = 1, 2, ..., n w1 , and set Witer=1, i=1, and enter the inner layer operation condition simulation optimization, i.e. step 3.2; Step 3.2: Use the energy storage battery output in step 2.1 to operate the service income J2 (P B (t)) is the optimal target, and the traditional hybrid frog leaping algorithm is used to calculate n w1 The advantages and disadvantages of the frog positions are sorted, and the inner frog positions are updated to iterate the optimal battery energy storage output curve; Step 3.3: Based on the output of the energy storage system represented by the optimal frog in the inner layer at the i-th frog position in the outer layer in step 3.2, calculate the objective function value J1(i) when the outer layer is at the i-th frog position; Step 3.4: Determine whether i is less than or equal to n w1 ; If yes, set i=i+1 and return to step 3.2; if no, proceed to step 3.5; Step 3.5: According to the outer layer n w1 The function values ​​of the frog positions {J1(1),J1(2),…,J1(n w1 )} size, and then distribute to n w2 subpopulations, and update the outer n w1 The position of the frogs; Step 3.6 determines whether Witer is less than or equal to WiterMax; if so, set Witer = Witer + 1, i = 1 and return to step 3.2.2; if not, proceed to step 3.7; Step 3.7: Based on the result of step 3.6, the outer optimization result can be obtained, that is, in the typical day scenario of the k1th new energy source and the k2th load, the battery energy storage system needs to be configured with x Best (k1, k2) minimum battery cells and y Best (k1, k2) minimum battery cells are connected in series; Step 4 uses the method in step 3 and the model in step 2 to calculate the battery energy storage system configuration plan for the typical daily scenario of K1 new energy sources and K2 loads in step 1, and forms a final capacity configuration plan; specifically, it includes the following steps: Step 4.1 Let the values ​​of k1 and k2 in step 3 traverse all scenarios of K1 new energy sources and K2 loads, and calculate the battery energy storage system configuration plan under each scenario, that is, the solution set H = {(x Best (k1,k2),y Best (k1,k2))|k1=1,2,…,K1,k2=1,2,…,K2}; In step 4.2, the configuration scheme with the most minimum battery units in the battery energy storage system of scheme set H is recorded as the most conservative configuration scheme of the battery energy storage system, and the configuration scheme with the least minimum battery units in the battery energy storage system of scheme set H is recorded as the most aggressive configuration scheme of the battery energy storage system, thereby completing the final capacity configuration of the battery energy storage system.

2. The method for optimizing the capacity configuration of an energy storage system taking into account the capacity of a battery module according to claim 1, characterized in that: The step 1 includes the following specific processes: Step 1.1: Measure the daily active power data of local renewable energy sources Pi Source (t), where i = 1, 2, ..., Num represents the number of days of renewable energy power generation in history, t = 1, 2, ..., T represents the time of each day, based on the Euclidean distance between daily active power generation Characterize the similarity between the renewable energy power generation on the i-th day and the j-th day, and use the clustering method to construct the active power curve of K1 renewable energy typical daily power generation scenarios; Step 1.2: Obtain the daily active power data Pi of the local load history Load (t), where i = 1, 2, ..., Num represents the number of days of historical load power consumption, t = 1, 2, ..., T represents the time of each day, based on the Euclidean distance between the daily load power consumption The similarity between the load power consumption on the i-th day and the j-th day is characterized, and the active power curves of K2 typical daily load power consumption scenarios are constructed using the clustering method.

3. The method for optimizing the capacity configuration of an energy storage system taking into account the capacity of a battery module according to claim 1, characterized in that: The step 3.2 includes the following specific processes: Step 3.2.1: Define the number of frogs in the inner population as n n1 , the number of inner subpopulations is n n2 , the maximum number of iterations NiterMax, randomly initialize the position of each frog in the inner optimization Z = [P B (1),P B (2),…,P B (T)], where the position of the jth frog is Z j =[P B,j (1),P B,j (2),…,P B,j (T)] represents the output power of the battery energy storage system throughout the day, j = 1, 2, ..., n n1 , the initial battery S SOE (1) = 0.5, and let Niter = 1, j = 1, and go to step 3.2.2; Step 3.2.2: In the formula is the objective function, the formula For the operation behavior of the energy storage battery, the objective function value J2(i,j) is calculated under the following constraints when the outer layer is the i-th frog position and the inner layer is the j-th frog position; System power balance constraints: Among them, P Grid (t), P Source (t), P Load (t) and P B (t) represents the power of the power grid, the power of new energy, the power of the load and the power of the battery at time t respectively; and the following constraints are met: The active power fluctuation range of the power grid needs to be within the specified value δ: The output power of the battery energy storage system at any moment must be limited to the rated power of the energy storage system: The energy state of the battery energy storage system should be kept at the lower limit at every moment and upper limit Within: Energy balance of battery energy storage system in a day: S SOE (1) = S SOE (T), The maximum range of battery energy storage system output change rate: Among them, λ represents the maximum multiple parameter of rated power, Battery energy storage system during integration for DC model voltage range: U - ≤yU e ≤U + , where y represents the number of battery minimum units connected in series, which is a positive integer, and U - and U + Respectively represent the upper and lower limits of DC voltage; During the integration of the battery energy storage system, the total number of the smallest battery units must be an integer multiple of their series connection: mod(x,y)=0, where mod(·) represents the function of finding the remainder. represents a positive integer; Step 3.2.3: Determine whether j is less than or equal to n n1 ; If yes, set j=j+1 and return to step 3.2.2; if no, proceed to step 3.3.4; Step 3.2.4: According to the outer layer is the i-th frog position, the inner layer n n1 The function values ​​of the frog positions {J2(i,1),J2(i,2),…,J2(i,n n1 )} size, and then distribute to n n2 subpopulations, and update the inner n n1 The specific process includes the following steps: Step 3.2.4.1: Sort from large to small. When the outer layer is the i-th frog position, the inner layer n n1 The function values ​​of the frog positions {J2(i,1),J2(i,2),…,J2(i,n n1 )}, for example, the frog corresponding to the maximum value is placed in the inner population 1, the frog corresponding to the second maximum value is placed in the inner population 2, and so on. n2 +1) maximum value of frogs are put back into the inner population 1 until all the inner population n n1 frogs are fully allocated to n n2 In the subpopulation, let s = 1; Step 3.2.4.2: The worst individual in the sth subpopulation jumps to the best individual in the same population and updates its position as follows: As shown, Assume that the worst individual in the sth population is Z s,Worest , the optimal individual is Z s,Best , then the frog's position after the update is expected to be Z' s,Worest , if the estimated updated position Z' s,Worest J2 is greater than the original position Z s,Worest If J2 is obtained, then go to step 3.2.4.5, otherwise go to step 3.2.4.3; where rand(0,1) represents a random number in the range [0,1]; Step 3.2.4.3: The worst individual in the sth subpopulation jumps to the best individual in all inner populations to update the position, as follows: As shown, Assume that the best individual in all populations is Z G,Best , then the frog's position after the update is expected to be Z' s,Worest , if the estimated updated position Z' s,Worest J2 is greater than the original position Z s,Worest If J2 is found, go to step 3.2.4.5, otherwise go to step 3.2.4.4; Step 3.2.4.4: Set the worst individual Z in the sth subpopulation s,Worest Using the following formula Generate a random new individual Z' s,Worest , proceed to step 3.3.4.5; where rand(-1,1) represents a random number in the range [-1,1]; Step 3.2.4.5: Determine whether s is less than or equal to n n2 ; If yes, set s=s+1 and return to step 3.2.4.2; if no, proceed to step 3.2.5; Step 3.2.5: Determine whether Niter is less than or equal to NiterMax; if so, set Niter = Niter + 1, j = 1 and return to step 3.2.2; if not, proceed to step 3.

3.

4. The method for optimizing the capacity configuration of an energy storage system taking into account the capacity of a battery module according to claim 1, characterized in that: The step 3.5 includes the following specific processes: Step 3.5.1: Sort the outer n from largest to smallest w1 The function values ​​of the frog positions {J1(1),J1(2),…,J1(n w1 ))}, for example, the frog corresponding to the maximum value is placed in the outer population 1, the frog corresponding to the second maximum value is placed in the outer population 2, and so on. w2 +1) maximum value of frogs are put back into the outer population 1 until all the outer population n w1 The outer frogs are fully distributed to n w2 In the subpopulation, let q = 1; Step 3.5.2: The worst individual in the qth subpopulation jumps to the best individual in the same population and updates its position, as shown in the following formula: Assume that the worst individual in the qth population is X q,Worest , the optimal individual is X s,Best , then the frog's position after the update is expected to be X' s,Worest , if the estimated updated position X' s,Worest J1 is greater than the original position X s,Best If J1 is obtained, then go to step 3.5.5, otherwise go to step 3.5.3; where randi(0,D1) represents a random integer in the range [0,D1]; Step 3.5.3: The worst individual in the qth subpopulation jumps to the best individual in all outer populations to update its position, as shown in the following formula: Assume that the best individual in all populations is X G,Best , then the frog's position after the update is expected to be X' s,Worest , if the estimated updated position X' s,Worest J1 is greater than the original position X s,Worest If J1 is found, go to step 3.5.5, otherwise go to step 3.5.4; Step 3.5.4: Take the worst individual X in the qth subpopulation s,Worest Using the following formula: Generate a random new individual X' s,Worest , go to step 3.5.5; where rand(-1,1) represents a random number in the range [-1,1]; Step 3.5.5: Determine whether q is less than or equal to n w2 ; If so, set q=q+1 and return to step 3.5.2; if not, proceed to step 3.6.

Images