Economic modeling and optimal configuration analysis method for load side energy storage system

Abstract

The invention discloses a load side energy storage system economy modeling and optimal configuration analysis method, which comprises the following steps: respectively establishing economy models of a single-type energy storage system and a hybrid energy storage system, and establishing an objective function and a constraint condition; simulation is carried out based on the single-type energy storage system economy model, an objective function curve is obtained, and sensitivity analysis is carried out; solving the optimal energy storage capacity and charging and discharging power by applying a genetic algorithm, so that the total cost of the energy storage system is lowest; optimizing and solving the configuration of the hybrid energy storage system by applying a particle swarm algorithm to obtain an appropriate hybrid energy storage rated capacity so as to obtain the optimal configuration of the energy storage system; on the premise of ensuring that energy storage configuration meets system requirements, a model is established by taking economy optimization as a target, optimal configuration parameters of the energy storage system are solved by constructing a target function and constraint conditions, and optimal energy storage capacity and charging and discharging power are determined, so that economy optimization configuration of the energy storage system is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of load-side energy storage systems, and specifically relates to a method for economic modeling and optimal configuration analysis of load-side energy storage systems. Background Art

[0002] Energy storage can be used as a backup power source on the load side to improve the reliability of power supply on the load side. It can also participate in system power regulation. When user demand is large and system supply is insufficient, the energy storage output power can alleviate system pressure and meet load-side demand in a timely manner. When new energy sources are connected to the grid in large quantities, energy storage can absorb electricity that the system cannot absorb, improve system power stability, and achieve demand response.

[0003] At present, the total installed capacity of load-side energy storage is relatively small and is still in its infancy. In order to ensure economy, it is necessary to obtain the optimal configuration of the energy storage system on the load side of the distribution network. However, there is currently no universal and effective economic model for the load-side energy storage system.

[0004] Therefore, in order to solve the above problems, it is necessary to develop an economic modeling and optimal configuration analysis method for the load-side energy storage system. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for economic modeling and optimal configuration analysis of a load-side energy storage system. Under the premise of ensuring that the energy storage configuration meets the system requirements, a model is established with economic optimization as the goal. By constructing an objective function and constraints, the optimal configuration parameters of the energy storage system are solved, and the optimal energy storage capacity and charge and discharge power are determined to achieve the economic optimization configuration of the energy storage system.

[0006] The object of the present invention is achieved as follows: A method for economic modeling and optimal configuration analysis of a load-side energy storage system comprises the following steps:

[0007] S1. Establish economic models of single-type and hybrid energy storage systems, and establish objective functions and constraints;

[0008] S11. Establish an economic model for a single-type energy storage system, with the minimum total cost of the energy storage system as the objective function and the constraints being the power shortage rate and the power surplus rate;

[0009] S12. Establish a multi-objective optimization mathematical model for the hybrid energy storage system, with the minimum total cost of the energy storage system as the objective function, and the constraints are power fluctuation limit and basic constraints for normal operation of the hybrid energy storage system;

[0010] S2. Simulate the economic model of a single-type energy storage system to obtain the objective function curve and perform sensitivity analysis;

[0011] S3, using genetic algorithm to solve the optimal energy storage capacity and charging and discharging power to minimize the total cost of the energy storage system;

[0012] S4. Apply the particle swarm algorithm to optimize the configuration of the hybrid energy storage system and obtain the appropriate rated capacity of the hybrid energy storage, thereby obtaining the optimal configuration of the energy storage system.

[0013] Furthermore, in step S11, the objective function is to minimize the total cost of the energy storage system, and the total cost of the energy storage system specifically includes initial investment cost, supporting facility cost, operation and maintenance cost, and replacement cost.

[0014] Furthermore, the initial investment cost C INI It is expressed as:

[0015] C INI =K e ×E ESS +K p ×P ESS

[0016] Where, E ESS is capacity configuration; P ESS is the maximum charge and discharge power; K e is the capacity cost factor, $ / MWh; K p is the power cost factor, $ / MW;

[0017] The supporting facilities cost C AF It is expressed as:

[0018] C AF =K af ×P ESS

[0019] Where K af is the supporting facilities cost coefficient, $ / MW; P ESS is the maximum charge and discharge power;

[0020] The operation and maintenance cost C OM It is expressed as:

[0021] C OM =K OM ×P ESS

[0022] Where K OM is the operation and maintenance cost coefficient, $ / MW / year; P ESS is the maximum charge and discharge power;

[0023] The replacement cost C REP It is expressed as:

[0024] C REP =(Ke ×E ESS +K p ×P ESS )×N REP

[0025] Where N REP is the number of replacements within the service life of the project, assuming that the service life of the energy storage system is L ESS , the project service life is L PROJ , N REP It is expressed as:

[0026]

[0027] ; In the formula, INT() represents the rounding function;

[0028] The total cost of the energy storage system is C SUM It is expressed as:

[0029] C SUM =C INI +C AF +C OM ·L PROJ +C REP .

[0031] Furthermore, in step S11, the objective function of minimizing the total cost of the energy storage system is specifically expressed as:

[0032] min:f(E ESS ,P ESS )=C INI +C AF +C OM +C REP

[0033] =(1+N REP )K e E ESS +(K p +K af +K OM L PROJ +N REP K p ) ESS .

[0035] Furthermore, in step S11, the power shortage rate and the power surplus rate are used as constraints, assuming that the system output power at time t is P RE (t), the load power is P load (t), the energy storage charging and discharging power is P ESS (t), greater than 0 means discharging, otherwise it means charging;

[0036] ① The power shortage rate LOEP is expressed as:

[0037]

[0038] ;t l Satisfy P load (t)>P RE (t);

[0039] Power shortage constraint

[0040] LOEP≤LOEP max

[0041] ; Among them, LOEP max It is the maximum power shortage rate limit value given according to the system power supply reliability requirements;

[0042] ② The electricity surplus rate SOEP is expressed as:

[0043]

[0044] ;t s Satisfy P load (t s ) <P RE (t s );

[0045] Power Surplus Constraint

[0046] SOEP≤SOEP max

[0047] Among them, SOEP max It is the maximum power surplus rate limit given according to the system power supply reliability requirements.

[0048] Furthermore, in step S12, the power fluctuation limit is used as a constraint condition, and the maximum 5-minute power change rate ΔP after smoothing the power fluctuation is 5m-max satisfy:

[0049] ΔP 5m-max ≤ΔP 5m-allowable

[0050] ; In the formula, ΔP 5m-allowable Indicates the maximum permissible 5-minute power change rate;

[0051] The basic constraints for normal operation of the hybrid energy storage system in step S12 specifically include state of charge constraints and power constraints, wherein ① the state of charge constraints are as follows:

[0052] SOC min ≤SOC(t)≤SOC max

[0053] ; In the formula, SOC min and SOC max They are the lower and upper limits of the state of charge constraint of the energy storage system respectively;

[0054] ②The power constraints are as follows:

[0055] Charging process:

[0056]

[0057] Discharge process:

[0058]

[0059] Where, P c,max (t) and P d,max (t) are the maximum allowable charging and discharging power of the energy storage system in the tth period, kW; P maxC and P maxD are the maximum continuous charging and discharging power of the energy storage system, kW; E C is the rated capacity of the energy storage system, Wh; δ represents the self-discharge rate of the energy storage system, % / hour; SOC(t-1) is the remaining power at the end of the t-1th period; η c and η d They are respectively the charging and discharging efficiency of the energy storage system, %.

[0060] Furthermore, the sensitivity analysis in step S2 specifically includes the relationship between the net benefit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net benefit of the wind-storage combined system and the penalty coefficient.

[0061] Furthermore, in step S3, the objective function of the single-type energy storage system is combined with the constraint conditions and converted into a fitness function and solved using a genetic algorithm;

[0062] The fitness function is expressed as follows:

[0063] f FIT =f(E ESS ,P ESS )+α·ABS(LOEP-LOEP max )+β·ABS(SOEP-SOEP max )

[0064] ; In the formula, ABS() is the absolute value function, f FIT is the fitness function of a single type energy storage system.

[0065] Furthermore, the application of the genetic algorithm to solve the problem in step S3 specifically includes the following steps:

[0066] (1) Optimize model initialization;

[0067] (2) Algorithm initialization; Maximum number of iterations N of the genetic algorithm max , and let the genetic algorithm execute the cycle number N gen =0;

[0068] (3) Chromosome encoding;

[0069] (4) Population initialization;

[0070] (5) Fitness evaluation;

[0071] (6) Selection, crossover and genetic operations;

[0072] (7) Produce a new generation of population;

[0073] (8) Genetic algorithm loop termination judgment; judgment of iteration termination condition N gen =N max Is it satisfied? If not, let the algorithm execute the loop iteration number N gen =N gen +1, return to step (6) to continue the loop calculation, if satisfied, directly execute the next step;

[0074] (9) Evaluation of the fitness of the new generation group;

[0075] (10) The algorithm ends; output the optimal individual (E ESS ,P ESS ).

[0076] Furthermore, the step S4 of applying the particle swarm algorithm to optimize the configuration of the hybrid energy storage system specifically includes the following steps:

[0077] (1) Population initialization;

[0078] (2) Calculate the initial extreme value of the population;

[0079] (3) Update the velocity and position of each particle;

[0080] (4) Determine whether the constraint conditions are met; if not, return to step (2) to continue the loop calculation; if satisfied, directly execute the next step;

[0081] (5) Update individual extreme values ​​and group extreme values ​​in the population;

[0082] (6) Determine whether the termination condition is met; if not, return to step (3) and continue the loop; if met, directly execute the next step;

[0083] (7) The algorithm ends; the optimal solution of the objective function is output.

[0084] Due to the adoption of the above technical scheme, the beneficial effects of the present invention are as follows: by establishing economic models of single-type and hybrid energy storage systems, determining objective functions and constraints, and performing sensitivity analysis, and then optimizing the single-type energy storage system by using a genetic algorithm and optimizing the hybrid energy storage system by using a particle swarm algorithm, an energy storage system optimization configuration model is established and simulation verification is performed, thereby optimizing the configuration of the energy storage system while ensuring power supply reliability with the purpose of improving the economy of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a flowchart of the present invention.

[0086] Figure 2 This is a flow chart of the genetic algorithm in step S3 of the present invention.

[0087] Figure 3 It is a flow chart of the particle swarm algorithm in step S4 of the present invention. DETAILED DESCRIPTION

[0088] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0089] like Figure 1 , Figure 2 , Figure 3 As shown, a method for economic modeling and optimal configuration analysis of a load-side energy storage system includes the following steps:

[0090] S1. Establish economic models of single-type and hybrid energy storage systems respectively, and establish objective functions and constraints.

[0091] S11. Establish an economic model for a single-type energy storage system, with the lowest total cost of the energy storage system as the objective function and the constraints being the power shortage rate and the power surplus rate.

[0092] Specifically, in step S11, the objective function is to minimize the total cost of the energy storage system, and the total cost of the energy storage system specifically includes initial investment cost, supporting facility cost, operation and maintenance cost, and replacement cost.

[0093] Specifically, the initial investment cost C INI It is expressed as:

[0094] C INI =K e ×E ESS +K p ×P ESS

[0095] Where, E ESS is capacity configuration; P ESS is the maximum charge and discharge power; K eis the capacity cost factor, $ / MWh; K p is the power cost factor, $ / MW.

[0096] Specifically, the supporting facilities cost C AF It is expressed as:

[0097] C AF =K af ×P ESS

[0098] Where K af is the supporting facilities cost coefficient, $ / MW; P ESS is the maximum charge and discharge power.

[0099] Specifically, the operation and maintenance cost C OM It is expressed as:

[0100] C OM =K OM ×P ESS

[0101] Where K OM is the operation and maintenance cost coefficient, $ / MW / year; P ESS is the maximum charge and discharge power.

[0102] Specifically, the replacement cost C REP It is expressed as:

[0103] C REP =(K e ×E ESS +K p ×P ESS )×N REP

[0104] Where N REP is the number of replacements within the service life of the project, assuming that the service life of the energy storage system is L ESS , the project service life is L PROJ , N REP It is expressed as:

[0105]

[0106] ; In the formula, INT() represents the integer function.

[0107] Specifically, the total cost C of the energy storage system SUM It is expressed as:

[0108] C SUM =C INI +C AF +C OM ·L PROJ +CREP .

[0110] Specifically, the objective function of minimizing the total cost of the energy storage system in step S11 is specifically expressed as:

[0111] min:f(E ESS ,P ESS )=C INI +C AF +C OM +C REP

[0112] =(1+N REP )K e E ESS +(K p +K af +K OM L PROJ +N REP K p ) ESS .

[0114] Specifically, in step S11, the power shortage rate and the power surplus rate are used as constraints, assuming that the system output power at time t is P RE (t), the load power is P load (t), the energy storage charging and discharging power is P ESS (t), greater than 0 indicates discharging, otherwise it indicates charging.

[0115] Specifically, the power shortage rate LOEP is expressed as:

[0116]

[0117] ;t l Satisfy P load (t)>P RE (t);

[0118] Insufficient power constraints

[0119] LOEP≤LOEP max

[0120] ; Among them, LOEP max It is the maximum power shortage rate limit value given according to the system power supply reliability requirements.

[0121] Specifically, the electricity surplus rate SOEP is expressed as:

[0122]

[0123] ;t s Satisfy P load (ts ) <P RE (t s );

[0124] The power surplus constraint

[0125] SOEP≤SOEP max

[0126] Among them, SOEP max It is the maximum power surplus rate limit given according to the system power supply reliability requirements.

[0127] S12. Establish a multi-objective optimization mathematical model for the hybrid energy storage system, with the minimum total cost of the energy storage system as the objective function and the constraints being power fluctuation limits and basic constraints for the normal operation of the hybrid energy storage system.

[0128] Specifically, in step S12, the power fluctuation limit is used as a constraint condition, and the maximum 5-minute power change rate ΔP after smoothing the power fluctuation is 5m-max satisfy:

[0129] ΔP 5m-max ≤ΔP 5m-allowable

[0130] ; In the formula, ΔP 5m-allowable Indicates the maximum permissible 5-minute power change rate.

[0131] Specifically, the basic constraints for normal operation of the hybrid energy storage system in step S12 specifically include state of charge constraints and power constraints, wherein ① the state of charge constraints are as follows:

[0132] SOC min ≤SOC(t)≤SOC max

[0133] ; In the formula, SOC min and SOC max They are the lower and upper limits of the state of charge constraint of the energy storage system respectively.

[0134] ②The power constraints are as follows:

[0135] Charging process:

[0136]

[0137] Discharge process:

[0138]

[0139] Where, P c,max (t) and P d,max (t) are the maximum allowable charging and discharging power of the energy storage system in the tth period, kW; PmaxC and P maxD are the maximum continuous charging and discharging power of the energy storage system, kW; E C is the rated capacity of the energy storage system, Wh; δ represents the self-discharge rate of the energy storage system, % / hour; SOC(t-1) is the remaining power at the end of the t-1th period; η c and η d They are respectively the charging and discharging efficiency of the energy storage system, %.

[0140] S2. Perform simulation based on the economic model of a single-type energy storage system to obtain the objective function curve and perform sensitivity analysis; the sensitivity analysis in step S2 specifically includes the relationship between the net profit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net profit of the wind-storage combined system and the penalty coefficient.

[0141] S3. Apply genetic algorithm to solve the optimal energy storage capacity and charging and discharging power to minimize the total cost of the energy storage system.

[0142] Specifically, in step S3, the objective function of the single-type energy storage system is combined with the constraint conditions and converted into a fitness function and solved using a genetic algorithm; the fitness function is expressed as follows:

[0143] f FIT =f(E ESS ,P ESS )+α·ABS(LOEP-LOEP max )+β·ABS(SOEP-SOEP max )

[0144] ; In the formula, ABS() is the absolute value function, f FIT is the fitness function of a single type energy storage system.

[0145] Specifically, the genetic algorithm is used to solve the problem in step S3, which specifically includes the following steps:

[0146] (1) Optimize model initialization.

[0147] (2) Algorithm initialization; Maximum number of iterations N of the genetic algorithm max , and let the genetic algorithm execute the cycle number N gen =0.

[0148] (3) Chromosome encoding; Since the problem to be solved is optimal (E ESS ,P ESS ), there are two variables, and floating point encoding can be used to improve the convergence accuracy and avoid falling into the local optimal solution.

[0149] (4) Population initialization: For each gene position of the chromosome, according to the variable (E ESS ,P ESS ) satisfies the value range, a gene is generated immediately, and then one bit at a time, a chromosome is generated, and the operation is repeated until a population of a specified size is generated.

[0150] (5) Fitness evaluation: The objective function transformed in the model solution is used as the fitness function, and the fitness value of each individual in the population is calculated in turn.

[0151] (6) Selection, crossover and genetic operations.

[0152] (7) Generate a new generation of population; by performing selection, crossover and mutation operations based on the previous generation of population, a new generation of population with the same population size can be generated, and the fitness function can be used to evaluate the fitness of each individual.

[0153] (8) Genetic algorithm loop termination judgment; judgment of iteration termination condition N gen =N max Is it satisfied? If not, let the algorithm execute the loop iteration number N gen =N gen +1, return to step (6) and continue the loop calculation. If satisfied, directly proceed to the next step.

[0154] (9) Fitness evaluation of the new generation group: Based on the principle that the individual with the maximum fitness is the optimal solution to the problem, the fitness of each individual in the current generation is calculated and evaluated, and the optimal individual and its corresponding optimal value are recorded and saved.

[0155] (10) The algorithm ends; output the optimal individual (E ESS ,P ESS ), since floating point encoding is adopted, the optimal result needs to be rounded to get the final optimization result and objective function value.

[0156] S4. Apply the particle swarm algorithm to optimize the configuration of the hybrid energy storage system and obtain the appropriate rated capacity of the hybrid energy storage, thereby obtaining the optimal configuration of the energy storage system.

[0157] Specifically, the step S4 of applying the particle swarm algorithm to optimize the hybrid energy storage system configuration specifically includes the following steps:

[0158] (1) Population initialization;

[0159] (2) Calculate the initial extreme value of the population;

[0160] (3) Update the velocity and position of each particle;

[0161] (4) Determine whether the constraint conditions are met; if not, return to step (2) to continue the loop calculation; if satisfied, directly execute the next step;

[0162] (5) Update individual extreme values ​​and group extreme values ​​in the population;

[0163] (6) Determine whether the termination condition is met; if not, return to step (3) and continue the loop; if met, directly execute the next step;

[0164] (7) The algorithm ends; the optimal solution of the objective function is output.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for economic modeling and optimal configuration analysis of load-side energy storage systems. Features: The following steps are involved: S1. Establish economic models of single-type and hybrid energy storage systems, and establish objective functions and constraints; S11. Establish an economic model for a single-type energy storage system, with the minimum total cost of the energy storage system as the objective function and the constraints being the power shortage rate and the power surplus rate; S12. Establish a multi-objective optimization mathematical model for the hybrid energy storage system, with the minimum total cost of the energy storage system as the objective function, and the constraints are power fluctuation limit and basic constraints for normal operation of the hybrid energy storage system; S2. Simulate the economic model of a single-type energy storage system to obtain the objective function curve and perform sensitivity analysis; S3, using genetic algorithm to solve the optimal energy storage capacity and charging and discharging power to minimize the total cost of the energy storage system; S4. Apply the particle swarm algorithm to optimize the configuration of the hybrid energy storage system and obtain the appropriate rated capacity of the hybrid energy storage, thereby obtaining the optimal configuration of the energy storage system.

2. According to claim 1, a method for economic modeling and optimal configuration analysis of a load-side energy storage system, Features: In step S11, the objective function is to minimize the total cost of the energy storage system. The total cost of the energy storage system specifically includes initial investment cost, supporting facility cost, operation and maintenance cost, and replacement cost.

3. According to claim 2, a method for economic modeling and optimal configuration analysis of a load-side energy storage system, Features: The initial investment cost C INI It is expressed as: C INI =K e ×E ESS +K p ×P ESS ; In the formula, E ESS is capacity configuration; P ESS is the maximum charge and discharge power; K e is the capacity cost factor, $ / MWh; K p is the power cost factor, $ / MW; The supporting facilities cost C AF It is expressed as: C AF =K af ×P ESS ; In the formula, K af is the supporting facilities cost coefficient, $ / MW; P ESS is the maximum charge and discharge power; The operation and maintenance cost C OM It is expressed as: C OM =K OM ×P ESS ; In the formula, K OM is the operation and maintenance cost coefficient, $ / MW / year; P ESS is the maximum charge and discharge power; The replacement cost C REP It is expressed as: C REP =(K e ×E ESS +K p ×P ESS )×N REP ; Where N REP is the number of replacements within the service life of the project, assuming that the service life of the energy storage system is L ESS , the project service life is L PROJ , N REP It is expressed as: ； In the formula, INT() represents the rounding function; The total cost of the energy storage system is C SUM It is expressed as: C SUM =C INI +C AF +C OM ·L PROJ +C REP 。 4. A method for economic modeling and optimal configuration analysis of a load-side energy storage system according to claim 3, Features: In step S11, the objective function of minimizing the total cost of the energy storage system is specifically expressed as: min:f(E ESS ,P ESS )=C INI +C AF +C OM +C REP =(1+N REP )K e E ESS +(K p +K af +K OM L PROJ +N REP K p )P ESS 。 5. According to claim 1, a method for economic modeling and optimal configuration analysis of a load-side energy storage system, Features: In step S11, the power shortage rate and the power surplus rate are used as constraints. Assume that the system output power at time t is P RE (t), the load power is P load (t), the energy storage charging and discharging power is P ESS (t), greater than 0 indicates discharging, otherwise it indicates charging; ① The power shortage rate LOEP is expressed as: ； t l Satisfy P load (t)>P RE (t); Power shortage constraint LOEP≤LOEP max ; Among them, LOEP max It is the maximum power shortage rate limit value given according to the system power supply reliability requirements; ② The electricity surplus rate SOEP is expressed as: ； t s Satisfy P load (t s ) <P RE (t s ); Power Surplus Constraint SOEP≤SOEP max ; Among them, SOEP max It is the maximum power surplus rate limit given according to the system power supply reliability requirements.

6. A method for economic modeling and optimal configuration analysis of a load-side energy storage system according to claim 1, Features: In step S12, the power fluctuation limit is used as a constraint condition, and the maximum 5-minute power change rate ΔP after the power fluctuation is smoothed 5m-max satisfy: ΔP 5m-max ≤ΔP 5m-allowable ; Where ΔP 5m-allowable Indicates the maximum permissible 5-minute power change rate; The basic constraints for normal operation of the hybrid energy storage system in step S12 specifically include state of charge constraints and power constraints, wherein ① the state of charge constraints are as follows: SOC min ≤SOC(t)≤SOC max ; In the formula, SOC min and SOC max are the lower and upper limits of the state of charge constraint of the energy storage system respectively; ②The power constraints are as follows: Charging process: Discharge process: ； Where P c,max (t) and P d,max (t) are the maximum allowable charging and discharging power of the energy storage system in the tth period, kW; P maxC and P maxD are the maximum continuous charging and discharging power of the energy storage system, kW; E C is the rated capacity of the energy storage system, Wh; δ represents the self-discharge rate of the energy storage system, % / hour; SOC(t-1) is the remaining power at the end of the t-1th period; η c and η d They are respectively the charging and discharging efficiency of the energy storage system, %.

7. A method for economic modeling and optimal configuration analysis of a load-side energy storage system according to claim 1, Features: The sensitivity analysis in step S2 specifically includes the relationship between the net benefit of the energy storage system and the rated capacity of the energy storage, the relationship between the optimal capacity of the energy storage system and the penalty coefficient, and the relationship between the maximum net benefit of the wind-storage combined system and the penalty coefficient.

8. A method for economic modeling and optimal configuration analysis of a load-side energy storage system according to claim 1, Features: In step S3, the objective function of the single-type energy storage system is combined with the constraint conditions and converted into a fitness function and solved using a genetic algorithm; The fitness function is expressed as follows: f FIT =f(E ESS ,P ESS )+α ABS(MAGNIFIER-MAGNIFIER max )+β·ABS(SOUP-SOUP max ); Where ABS() is the absolute value function, f FIT is the fitness function of a single type energy storage system.

9. A method for economic modeling and optimal configuration analysis of a load-side energy storage system according to claim 1, Features: The genetic algorithm is used to solve the problem in step S3, which specifically includes the following steps: (1) Optimize model initialization; (2) Algorithm initialization; Maximum number of iterations N of the genetic algorithm max , and let the genetic algorithm execute the cycle number N gen =0; (3) Chromosome encoding; (4) Population initialization; (5) Fitness evaluation; (6) Selection, crossover and genetic operations; (7) Produce a new generation of population; (8) Genetic algorithm loop termination judgment; judgment of iteration termination condition N gen =N max Is it satisfied? If not, let the algorithm execute the loop iteration number N gen =N gen +1, return to step (6) to continue the loop calculation, if satisfied, directly execute the next step; (9) Evaluation of the fitness of the new generation group; (10) The algorithm ends; output the optimal individual (E ESS ,P ESS ).

10. A method for economic modeling and optimal configuration analysis of a load-side energy storage system according to claim 1, Features: The step S4 in which the particle swarm algorithm is applied to optimize the configuration of the hybrid energy storage system specifically includes the following steps: (1) Population initialization; (2) Calculate the initial extreme value of the population; (3) Update the velocity and position of each particle; (4) Determine whether the constraint conditions are met; if not, return to step (2) to continue the loop calculation; if satisfied, directly execute the next step; (5) Update individual extreme values ​​and group extreme values ​​in the population; (6) Determine whether the termination condition is met; if not, return to step (3) and continue the loop; if met, directly execute the next step; (7) The algorithm ends; the optimal solution of the objective function is output.

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