Energy storage configuration method for suppressing influence of super fast charging pile access on voltage stability of power distribution network

Abstract

The invention relates to an energy storage configuration method for suppressing the influence of super fast charging pile access on the voltage stability of a power distribution network. The method comprises the following steps: step 1, monitoring and collecting power data in a charging process of a super fast charging pile in real time; step 2, establishing a mathematical model of peak pulse load characteristics by using the data collected in the step 1; 3, performing model parameter estimation on the peak pulse mathematical model established in the step 2; 4, verifying the model by using a new measured data set; and step 5, performing optimal configuration on the energy storage system according to the verified spike pulse characteristic model in the step 4 and the voltage stability requirement of the power distribution network. The method can effectively inhibit the influence of the spike pulse on the stability of the power grid, and ensures the economic operation and stability of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and relates to a method for analyzing voltage stability when a super-fast charging pile is connected to a distribution network and a corresponding energy storage configuration strategy, and in particular, an energy storage configuration method for suppressing the impact of super-fast charging pile access on the voltage stability of a distribution network. Background Art

[0002] With the rapid popularization of electric vehicles (EVs), ultra-fast charging stations (UFCS), as key infrastructure supporting fast charging of EVs, have posed new challenges to grid stability due to their access and operation in distribution networks.

[0003] During the charging process, especially when starting and stopping charging, super-fast charging piles will generate spike pulses with high instantaneous power and short duration. These spike pulses may cause transient voltage fluctuations, frequency deviations and other problems in the distribution network, affecting the static and transient stability of the distribution network. In the existing distribution network design, this peak load characteristic brought by super-fast charging piles is usually not fully considered.

[0004] Traditional load forecasting and management methods are often based on average load models, which make it difficult to accurately predict and respond to the impact of spike pulses. In addition, as the demand for electric vehicle charging grows, multiple charging piles are increasingly connected to the distribution network, and the superposition effect of spike pulses will further aggravate the problem of grid stability.

[0005] At present, the power industry's research on super-fast charging piles connected to the distribution network is mainly focused on charging strategy optimization, grid upgrade and transformation, etc. However, there are relatively few studies on energy storage configuration methods for spike pulse characteristics, especially in the optimization of voltage stability.

[0006] Therefore, how to develop a storage configuration method that can effectively suppress the impact of spike pulses and improve the voltage stability of the distribution network is of great significance for ensuring the safety of the power grid and promoting the development of the electric vehicle industry.

[0007] After searching, no published technical documents identical or similar to the present invention were found. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art. In view of the voltage stability problem that may be caused when electric vehicle fast charging facilities, especially ultra-fast charging stations (UFCS) are connected to the distribution network, a storage energy configuration method for suppressing the influence of ultra-fast charging pile access on the voltage stability of the distribution network is proposed. By analyzing the peak pulse characteristics of the ultra-fast charging pile and combining the voltage stability requirements of the power grid, the configuration of the energy storage system is optimized to achieve effective suppression of the peak pulse, thereby improving the stability and reliability of the distribution network, effectively suppressing the influence of the peak pulse on the stability of the power grid, and at the same time ensuring the economic operation and stability of the distribution network.

[0009] The present invention solves the practical problem by adopting the following technical solutions:

[0010] A method for configuring energy storage for suppressing the impact of super-fast charging pile access on the voltage stability of a distribution network comprises the following steps:

[0011] Step 1: Real-time monitoring and collection of power data during the charging process of the super-fast charging pile;

[0012] Step 2: Using the data collected in step 1, a mathematical model of the peak pulse load characteristics is established;

[0013] Step 3, estimating model parameters of the spike pulse mathematical model established in step 2;

[0014] Step 4: Use the new measured data set to verify the model;

[0015] Step 5: Optimize the configuration of the energy storage system according to the spike pulse characteristic model verified in step 4 and the voltage stability requirements of the distribution network;

[0016] Moreover, the specific method of step 1 is:

[0017] High-precision power monitoring equipment is installed on the super-fast charging pile to monitor and collect power data during the charging process in real time, including power readings at the beginning and end of charging, as well as instantaneous power changes during the charging process.

[0018] Moreover, the specific method of step 2 is:

[0019] Select a mathematical model to describe the spike pulse P L (t), use a piecewise linear model to describe the rise and fall process of the spike pulse:

[0020]

[0021] Among them, P base : basic power; A: rising rate; B: falling rate; P peak : peak power; t start ,tpeak ,t end : The start time, peak time, and end time of the spike pulse.

[0022] Moreover, the specific method of step 3 is:

[0023] The parameters of the rising rate A and falling rate B in the model are determined by fitting the actual collected power data. The rates of the power rising and falling processes are estimated respectively:

[0024]

[0025] Moreover, the specific method of step 4 is:

[0026] Use the new measured data set to verify the model and calculate the prediction error E:

[0027]

[0028] Where, N: the number of data points; P: model (t i ): power predicted by the model; P actual (t i ): The actual measured power.

[0029] Moreover, the specific steps of step 5 include:

[0030] Step 5.1: Construct the objective function:

[0031] Minimize the total cost of the energy storage system, including initial investment cost, operating cost and maintenance cost:

[0032] min C total =C cap (E rated ,P rated )+C op (E rated ,P rated )+C maint (E rated ,P rated ) (5)

[0033] Step 5.2: Create constraints:

[0034] (1) Capacity constraints:

[0035]

[0036] (2) Power Constraints:

[0037]

[0038] (3) Voltage stability constraints:

[0039] 1) The first type of static voltage stability index SVSI:

[0040]

[0041] Among them, U i is the voltage at node i; R ij is the resistance of branch ij; X ij is the reactance of branch ij; P j is the active power of node j; Q j is the reactive power of node j.

[0042] 2) The second type of static voltage stability index

[0043]

[0044] Among them, S j is the apparent power of node j; U j is the voltage at node j; I R, i is I i The real part of I I, i is I i The imaginary part of ΔU i The voltage difference between two adjacent power flow calculations obtained by changing the reactive power of node i.

[0045] (4) Energy storage charging and discharging efficiency constraints:

[0046]

[0047] (5) Energy storage response time constraints:

[0048] T response ≤T max (11)

[0049] (6) Safety and reliability constraints:

[0050]

[0051] Step 5.3: Optimize and solve the model to obtain the energy storage system configuration parameters.

[0052] Moreover, the specific steps of step 5.3 include:

[0053] Step 5.3.1: Initialize the population and randomly generate an initial population N of energy storage system configuration parameters, where each individual represents a potential configuration scheme;

[0054] Step 5.3.2: Set the key parameter capacity E of the energy storage system rated and power Prated Encoded as chromosomes;

[0055] Step 5.3.3: Define the fitness function F(x) and evaluate the fitness of each individual according to the objective function and constraints;

[0056] Step 5.3.4: Select according to the fitness of the individual. Excellent individuals have a higher probability of being selected into the next generation; the selection probability P i It can be expressed as:

[0057] Step 5.3.5: Crossover the selected individuals to generate new offspring to explore the solution space and retain excellent genetic characteristics; the crossover point k is randomly selected, and the offspring x after crossover is i ′ is expressed as:

[0058] Step 5.3.6: Mutate the chromosome of the individual with a certain probability and introduce a new solution; the mutation is achieved by randomly flipping the bits on the chromosome:

[0059] Step 5.3.7: Based on the selection, crossover and mutation operations, a new population is generated to prepare for the next round of iteration.

[0060] Step 5.3.8: Repeat steps 5.3.4 to 5.3.7 until the termination condition is met, such as reaching the predetermined number of iterations G. max or the fitness improvement is less than the threshold ΔF;

[0061] Step 5.3.9: During the iteration process, record and save the optimal individual, which has the lowest total cost while satisfying all constraints;

[0062] Step 5.3.10: Decode the final optimal individual and convert it back to the actual energy storage system configuration parameters.

[0063] Advantages and beneficial effects of the present invention:

[0064] 1. The present invention proposes a method for energy storage configuration to suppress the impact of super-fast charging pile access on the voltage stability of the distribution network, which significantly improves the voltage stability of the distribution network when the super-fast charging pile is connected. By real-time monitoring and collecting power data during the charging process of the super-fast charging pile (step 1), and using these data to establish a mathematical model of the peak pulse load characteristics (step 2), the present invention can accurately capture and predict the occurrence of peak pulses, thereby providing a scientific basis for the optimal configuration of the energy storage system. Accurate estimation of model parameters (step 3) and model verification of new data sets (step 4) further ensure the accuracy and effectiveness of the energy storage system configuration. This strategy not only improves the reliability of the power grid and reduces the risk of power outages, but also improves the overall operating efficiency of the power grid by optimizing energy distribution. In addition, the implementation of the present invention reduces dependence on traditional power grid infrastructure, reduces the cost of upgrading and transformation, and at the same time extends the service life of energy storage equipment, achieving a dual improvement in economic and environmental benefits. With the popularization of electric vehicles and the use of renewable energy, the present invention provides a flexible and sustainable operation model for power grid operators, supports the widespread access to green energy, and promotes the optimization and upgrading of energy structure.

[0065] 2. The present invention proposes a voltage stability optimization method for super-fast charging piles connected to the distribution network. The method proposes a storage system configuration strategy by deeply analyzing the peak pulse characteristics generated by the super-fast charging piles during the charging process. The core of this strategy is to achieve the economy and environmental sustainability of the energy storage system configuration by constructing an objective function (step 5.1) and formulating constraints (step 5.2). Solving the optimization model (step 5.3) further ensures that the energy storage system can meet the voltage stability requirements while having the necessary performance indicators. This method can not only accurately predict and suppress the potential impact of spike pulses on grid stability, but also flexibly adjust the response strategy of the energy storage system according to the real-time grid operation status and changes in electric vehicle charging demand. By optimizing the scale and operation mode of the energy storage system, the present invention reduces operating costs and reduces negative impacts on the environment. Ultimately, the present invention aims to provide distribution network operators with an efficient, economical and environmentally friendly solution to support the rapid development of electric vehicles while ensuring the long-term stable operation of the power grid. DETAILED DESCRIPTION

[0066] The following further describes the embodiments of the present invention:

[0067] A method for configuring energy storage for suppressing the impact of super-fast charging pile access on the voltage stability of a distribution network comprises the following steps:

[0068] Step 1: Real-time monitoring and collection of power data during the charging process of the super-fast charging pile;

[0069] The specific method of step 1 is:

[0070] High-precision power monitoring equipment is installed on the super-fast charging pile to monitor and collect power data during the charging process in real time, including power readings at the beginning and end of charging, as well as instantaneous power changes during the charging process.

[0071] Step 2: Using the data collected in step 1, a mathematical model of the peak pulse load characteristics is established;

[0072] The specific method of step 2 is:

[0073] Select a mathematical model to describe the spike pulse P L (t), use a piecewise linear model to describe the rise and fall process of the spike pulse:

[0074]

[0075] Among them, P base : basic power (unit: kW); A: rising rate (unit: kW / s); B: falling rate (unit: kW / s); P peak : Peak power (unit: kW); t start ,t peak ,t end : The start time, peak time, and end time of the spike pulse (unit: seconds).

[0076] In this embodiment, in order to accurately capture the characteristics of the spike pulses generated by the super-fast charging pile during the charging process, the present invention proposes a comprehensive modeling method based on measured data and theoretical analysis. The core of this method is to collect detailed power change data through sensors installed on the super-fast charging pile, which includes power readings at the beginning and end of charging, as well as instantaneous power changes during charging. With these data, a mathematical model is established to describe the characteristics of the spike pulses, including their amplitude, duration, and frequency.

[0077] The mathematical model of the peak pulse load characteristics should be able to accurately describe the key characteristics of the peak pulse such as amplitude, duration and frequency, and provide a scientific basis for the configuration of the energy storage system. L (t), a piecewise linear model is used to describe the rise and fall of the spike pulse.

[0078] Step 3, estimating model parameters of the spike pulse mathematical model established in step 2;

[0079] The specific method of step 3 is:

[0080] The parameters of the rising rate A and falling rate B in the model are determined by fitting the actual collected power data:

[0081] For the rates of power increase and decrease, estimate:

[0082]

[0083] Step 4: Use the new measured data set to verify the model and evaluate the accuracy of the model prediction.

[0084] The prediction error E is calculated to ensure that the model can accurately reflect the actual impact of spike pulses on the voltage stability of the distribution network.

[0085] The specific method of step 4 is:

[0086] Use the new measured data set to verify the model. Calculate the prediction error E:

[0087]

[0088] Where, N: the number of data points; P: model (t i ): power predicted by the model; P actual (t i ): The actual measured power.

[0089] Step 5: Optimize the configuration of the energy storage system according to the spike pulse characteristic model verified in step 4 and the voltage stability requirements of the distribution network;

[0090] The step 5 of optimizing the configuration of the energy storage system includes determining key parameters of the energy storage system such as capacity, power, response time, and formulating corresponding charging and discharging strategies to ensure that peak pulses can be effectively suppressed when super-fast charging piles are connected to maintain the stability of the power grid.

[0091] Energy storage system configuration strategy based on peak pulse characteristics, including the optimal selection of parameters such as capacity, power and response time of energy storage system. Voltage stability is a key consideration when building an energy storage system configuration optimization model. In order to ensure the voltage stability of the power grid during peak loads, it is necessary to include voltage stability constraints in the optimization model.

[0092] The specific steps of step 5 include:

[0093] Step 5.1: Construct the objective function:

[0094] Minimize the total cost of the energy storage system, including initial investment cost, operating cost and maintenance cost:

[0095] min C total =C cap (E rated ,P rated )+C op (Erated ,P rated )+C maint (E rated ,P rated ) (5)

[0096] In this embodiment, the configuration optimization of the energy storage system aims to minimize the total cost of the system. The objective function comprehensively considers the initial investment cost, operating cost and maintenance cost of the energy storage system. The initial investment cost covers the cost of purchasing, installing and commissioning the energy storage equipment; the operating cost includes the cost of daily operation, monitoring and energy circulation; and the maintenance cost involves the cost of regular inspection, fault repair and equipment replacement. The objective function is designed to maximize economic benefits while ensuring that the energy storage system can effectively suppress the spike pulses caused by the access of super-fast charging piles and maintain the voltage stability of the distribution network.

[0097] Step 5.2: Create constraints:

[0098] (1) Capacity constraint: ensuring that the energy storage system can provide sufficient energy during peak load periods.

[0099]

[0100] (2) Power constraint: Ensure that the rated power of the energy storage system is higher than the peak power of the peak load.

[0101]

[0102] (3) Voltage stability constraints:

[0103] 1) The first type of static voltage stability index (SVSI):

[0104] In order to evaluate the voltage stability of the power grid under the current operating state, the first type of static voltage stability index is used. This index is based on the existence of the solution of the power flow equation to ensure that all branches The values ​​are all less than 1. The mathematical expressions and criteria are as follows:

[0105]

[0106] Among them, U i is the voltage at node i; R ij is the resistance of branch ij; X ij is the reactance of branch ij; P j is the active power of node j; Q j is the reactive power of node j.

[0107] 2) Constraints on the ability to resist small disturbances:

[0108] The second type of static voltage stability index is used to evaluate the change trend of the system after a small disturbance. This index is based on the voltage and current characteristics of the load and power supply to ensure that the second type of static voltage stability index of the system is stable under any disturbance. It must be less than 1 to ensure the stability of the system after disturbance. The mathematical expression and criterion are as follows:

[0109]

[0110] Among them, S j is the apparent power of node j; U j is the voltage at node j; I R, i is I i The real part of I I, i is I i The imaginary part of ΔU i The voltage difference between two adjacent power flow calculations obtained by changing the reactive power of node i.

[0111] (4) Energy storage charging and discharging efficiency constraints: Consider the charging and discharging efficiency of the energy storage system to ensure the actual operating power and capacity.

[0112]

[0113] (5) Energy storage response time constraint: The response time of the energy storage system should meet the dynamic needs of the power grid.

[0114] T response ≤T max (11)

[0115] (6) Safety and reliability constraints: Ensure the safety and reliability of the energy storage system under extreme conditions.

[0116]

[0117] In this embodiment, in the process of optimizing the configuration of the energy storage system, the present invention sets a series of constraints to ensure that the system has the necessary performance indicators while meeting the voltage stability requirements. These constraints include the capacity, power output, voltage stability index, charging and discharging efficiency, response time of the energy storage system, and the safety and reliability of the system. Capacity constraints ensure that the energy storage system can store enough energy to cope with peak loads; power constraints ensure that the system can provide the necessary power to stabilize the power grid; voltage stability index constraints are used to evaluate and maintain the stability of the power grid under various operating conditions; charging and discharging efficiency constraints focus on the energy utilization efficiency of the system; response time constraints ensure that the system can respond quickly to changes in power grid demand; and safety and reliability constraints ensure the stability and safety of the system under extreme conditions and long-term operation.

[0118] Step 5.3: Optimize and solve the model to obtain the configuration parameters of the energy storage system;

[0119] In this embodiment, the optimization model solving process is an iterative process, which aims to accurately configure the energy storage system to suppress the impact of spike pulses generated when the super-fast charging pile is connected to the distribution network. In order to implement the optimal configuration of the energy storage system, a genetic algorithm (GA) is selected as a heuristic search algorithm. The genetic algorithm imitates the process of natural selection and finds the optimal solution to the problem through selection, crossover and mutation operations in an iterative process. In the energy storage system configuration optimization, GA is used to find the best configuration that meets specific performance indicators (such as voltage stability, cost minimization, etc.). The following are the detailed steps for optimizing the configuration of the energy storage system using GA.

[0120] The specific steps of step 5.3 include:

[0121] Step 5.3.1: Initialize the population. Randomly generate an initial population N of energy storage system configuration parameters, where each individual represents a potential configuration scheme.

[0122] Step 5.3.2: The key parameters of the energy storage system (capacity E rated and power P rated ) are encoded as chromosomes for processing in a genetic algorithm.

[0123] Step 5.3.3: Define the fitness function F(x) and evaluate the fitness of each individual according to the objective function (minimizing the total cost) and constraints (such as voltage stability, charge and discharge efficiency, etc.).

[0124] Step 5.3.4: Selection operation. Selection is performed based on the fitness of the individual. Excellent individuals have a higher probability of being selected into the next generation. Selection probability P i It can be expressed as:

[0125] Step 5.3.5: Crossover operation. Crossover is performed on the selected individuals to generate new offspring to explore the solution space and retain excellent genetic characteristics. The crossover point k can be randomly selected, and the offspring x after crossover is i ′ can be expressed as:

[0126] Step 5.3.6: Mutation operation. Mutate the chromosome of the individual with a certain probability to introduce new solutions to avoid premature convergence. Mutation can be achieved by randomly flipping the bits on the chromosome:

[0127] Step 5.3.7: New population generation: Based on the selection, crossover and mutation operations, a new population is generated to prepare for the next round of iterations.

[0128] Step 5.3.8: Repeat steps 5.3.4 to 5.3.7 until the termination condition is met, such as reaching the predetermined number of iterations G. max Or the fitness improvement is less than the threshold ΔF.

[0129] Step 5.3.9: Identification of the optimal solution. During the iteration process, the optimal individual is recorded and saved, which has the lowest total cost while satisfying all constraints.

[0130] Step 5.3.10: Post-processing and output: Decode the final optimal individual, convert it back to the actual energy storage system configuration parameters, and perform necessary verification and testing to ensure its effectiveness and reliability in practical applications.

[0131] Through genetic algorithms, the optimal configuration of energy storage systems can be effectively found in a large search space, taking into account multiple factors such as voltage stability, cost and performance. This method is suitable for complex optimization problems, especially when the problem is difficult to solve accurately using traditional mathematical methods.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0133] The present application is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, and the combination of the process and / or box in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.

[0134] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

Claims

1. A method for energy storage configuration to suppress the impact of super-fast charging pile access on the voltage stability of the distribution network, characterized by: The following steps are involved: Step 1: Real-time monitoring and collection of power data during the charging process of the super-fast charging pile; Step 2: Using the data collected in step 1, a mathematical model of the peak pulse load characteristics is established; Step 3, estimating model parameters of the spike pulse mathematical model established in step 2; Step 4: Use the new measured data set to verify the model; Step 5: Optimize the configuration of the energy storage system based on the spike pulse characteristic model verified in step 4 and the voltage stability requirements of the distribution network.

2. According to claim 1, a method for energy storage configuration for suppressing the impact of super-fast charging pile access on the voltage stability of the distribution network is characterized by: The specific method of step 1 is: High-precision power monitoring equipment is installed on the super-fast charging pile to monitor and collect power data during the charging process in real time, including power readings at the beginning and end of charging, as well as instantaneous power changes during the charging process.

3. According to claim 1, a method for energy storage configuration for suppressing the impact of super-fast charging pile access on the voltage stability of the distribution network is characterized by: The specific method of step 2 is: Select a mathematical model to describe the spike pulse P L (t), use a piecewise linear model to describe the rise and fall process of the spike pulse: Among them, P base : basic power; A: rising rate; B: falling rate; P peak : peak power; t start ,t peak ,t end : The start time, peak time, and end time of the spike pulse.

4. According to claim 1, a method for configuring energy storage for suppressing the impact of super-fast charging pile access on the voltage stability of the distribution network is characterized by: The specific method of step 3 is: The parameters of the rising rate A and falling rate B in the model are determined by fitting the actual collected power data. The rates of the power rising and falling processes are estimated respectively:

5. According to claim 1, a method for energy storage configuration for suppressing the impact of super-fast charging pile access on the voltage stability of the distribution network is characterized by: The specific method of step 4 is: Use the new measured data set to verify the model and calculate the prediction error E: Where, N: the number of data points; P: model (t i ): power predicted by the model; P actual (t i ): The actual measured power.

6. The energy storage configuration method for suppressing the impact of super-fast charging pile access on the voltage stability of the distribution network according to claim 1 is characterized by: The specific steps of step 5 include: Step 5.1: Construct the objective function: Minimize the total cost of the energy storage system, including initial investment cost, operating cost and maintenance cost: my C total =C cap (E rated ,P rated )+C op (E rated ,P rated )+C maint (E rated ,P rated ) (5) Step 5.2: Create constraints: (1) Capacity constraints: (2) Power Constraints: (3) Voltage stability constraints: 1) The first type of static voltage stability index SVSI: Among them, U i is the voltage at node i; R ij is the resistance of branch ij; X ij is the reactance of branch ij; P j is the active power of node j; Q j is the reactive power of node j; 2) The second type of static voltage stability index Among them, S j is the apparent power of node j; U j is the voltage at node j; I R, i is I i The real part of I I, i is I i The imaginary part of ΔU i The voltage difference between two adjacent power flow calculations obtained by changing the reactive power of node i; (4) Energy storage charging and discharging efficiency constraints: (5) Energy storage response time constraints: T response ≤T max (11) (6) Safety and reliability constraints: Step 5.3: Optimize and solve the model to obtain the energy storage system configuration parameters.

7. The energy storage configuration method for suppressing the impact of super-fast charging pile access on the voltage stability of the distribution network according to claim 6, characterized in that: The specific steps of step 5.3 include: Step 5.3.1: Initialize the population and randomly generate an initial population N of energy storage system configuration parameters, where each individual represents a potential configuration scheme; Step 5.3.2: Set the key parameter capacity E of the energy storage system rated and power P rated Encoded as chromosomes; Step 5.3.3: Define the fitness function F(x) and evaluate the fitness of each individual according to the objective function and constraints; Step 5.3.4: Select according to the fitness of the individual. Excellent individuals have a higher probability of being selected into the next generation; the selection probability P i It can be expressed as: Step 5.3.5: Crossover the selected individuals to generate new offspring to explore the solution space and retain excellent genetic characteristics; the crossover point k is randomly selected, and the offspring x after crossover is i ′ is expressed as: Step 5.3.6: Mutate the chromosome of the individual with a certain probability and introduce a new solution; the mutation is achieved by randomly flipping the bits on the chromosome: Step 5.3.7: Generate a new population based on selection, crossover and mutation operations, and prepare for the next round of iteration; Step 5.3.8: Repeat steps 5.3.4 to 5.3.7 until the termination condition is met, such as reaching the predetermined number of iterations G. max or the fitness improvement is less than the threshold ΔF; Step 5.3.9: During the iteration process, record and save the optimal individual, which has the lowest total cost while satisfying all constraints; Step 5.3.10: Decode the final optimal individual and convert it back to the actual energy storage system configuration parameters.

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