A highway photovoltaic sound barrier storage and charging system optimization configuration method based on an improved particle swarm algorithm

Abstract

This invention relates to an optimized configuration method for a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm. Based on photovoltaic power output and electricity consumption, the method obtains the battery charge curve, uses rainflow counting for data compression and cycle number extraction, and then calculates the equivalent lifespan using the equivalent cycle life method. It analyzes and constructs constraints and establishes a capacity configuration model for the highway photovoltaic sound barrier energy storage and charging system that considers energy storage lifespan. By improving the inertia weight, the global and local search capabilities of the particles are enhanced, aiming to obtain the optimal number of batteries and charging piles for the system configuration. This invention enhances the local and global search capabilities of the particles by dynamically adjusting their inertia weight, and predicts the battery lifespan based on the hourly state of charge, thus achieving a capacity configuration method that better reflects engineering realities.

Description

Technical Field

[0001] This invention relates to an optimized configuration method for a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm, belonging to the field of transportation and energy integration technology. Background Technology

[0002] Social progress and economic development are leading to a gradual shortage of traditional non-renewable energy sources such as coal, making energy transition a major concern worldwide. my country possesses vast unused road space, and integrating distributed clean energy technologies into road infrastructure is becoming a hot research topic. Installing photovoltaic modules on sound barriers along urban roadsides, along with battery banks and electric bicycle charging stations at suitable locations, is a concrete solution for achieving efficient and self-sufficient energy in transportation. This necessitates addressing the issue of optimizing system equipment capacity.

[0003] Currently, the "photovoltaic-storage-charging" system is used to address the challenge of optimizing system equipment capacity. Establishing the system model and optimizing capacity configuration are core issues that need to be addressed to improve the system's energy self-sufficiency. A common approach is to use particle swarm optimization (PSO) to calculate system capacity configuration schemes to minimize investment costs. PSO is a stochastic optimization method based on social behavior simulation, widely used in engineering due to its simple principle, ease of implementation, high accuracy, and fast convergence. However, in practical applications, to ensure a continuous and stable power supply for users, a certain scale of energy storage equipment needs to be configured in the power generation system. Traditional energy storage equipment lifespan is usually considered to be a fixed number of years, which deviates significantly from reality. The lifespan of energy storage equipment is closely related to its charging and discharging power, which directly affects the system's investment cost. Therefore, simply considering the initial investment of the system equipment is clearly insufficient. Furthermore, the standard PSO algorithm uses constant inertial weights, which cannot be dynamically adjusted according to the particle's position, easily causing the results to diverge from the optimal region of the solution set.

[0004] A review of domestic and international research on the optimization of photovoltaic energy storage capacity configuration reveals that most studies focus on optimizing the configuration of photovoltaic and energy storage devices based on a given electricity load. However, there is a lack of research on how to optimize the load and energy storage capacity configuration for a given photovoltaic output. Therefore, it is urgent to propose a systematic configuration method and control strategy to address this issue. Summary of the Invention

[0005] This invention provides an optimized configuration method for a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm. By dynamically adjusting the inertia weight of the particle swarm, the local and global search capabilities of the particles are enhanced. At the same time, the battery life is predicted based on the hourly state of charge of the battery, so as to achieve a capacity configuration method that is more in line with engineering practice.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] An optimized configuration method for a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm includes the following steps:

[0008] Step S1: Establish a comprehensive energy input and output model for the highway photovoltaic sound barrier energy storage and charging system, which includes a mathematical model of photovoltaic power generation, a mathematical model of energy storage battery, and charging piles located on the side of the highway to provide power to electric bicycles;

[0009] Step S2: Determine the number of photovoltaic modules in the photovoltaic power generation mathematical model, the number of batteries and charging piles in the energy storage battery mathematical model, construct the objective function of the energy input and output integrated model based on the equivalent life cost of the battery, and set the constraints of the energy input and output integrated model.

[0010] Step S3: Based on the distance between the particle position and the optimal position, dynamically adjust the inertia weight to construct an adaptive inertia weight particle swarm algorithm;

[0011] Step S4: Under the objective function of the energy input-output integrated model and the set constraints, the configuration of the highway photovoltaic sound barrier energy storage and charging system is solved using the constructed adaptive inertial weighted particle swarm algorithm to obtain the optimal equipment capacity configuration.

[0012] As a further preferred embodiment of the present invention, in the energy input-output integrated model established in step S1, the photovoltaic module of the photovoltaic power generation mathematical model utilizes solar clean energy to convert light energy into electrical energy, which serves as the input source for the highway photovoltaic sound barrier energy storage and charging system.

[0013] The mathematical model of the energy storage battery shows that the battery, as an energy storage device, can realize bidirectional energy flow;

[0014] Electric bicycle charging stations draw clean solar energy directly from the batteries.

[0015] As a further preferred embodiment of the present invention, the objective function in step S2 includes the photovoltaic output of the photovoltaic module, and the calculation formula is as follows:

[0016]

[0017] In formula (1), This represents the real-time power generation of the photovoltaic modules. ; Solar radiation intensity, ; The operating temperature of the battery. ; For ambient reference temperature, 25 ; This indicates the maximum output power of the photovoltaic module under standard test conditions. ; Indicates the light intensity under standard conditions. ; The power-temperature conversion factor, / ;

[0018] As a further preferred embodiment of the present invention, the number of batteries and charging piles in the objective function of step S2 satisfies the following combination:

[0019] ,

[0020] ,

[0021] (2)

[0022] In formula (2), For the number of photovoltaic modules, The number of charging stations, For the number of batteries, This refers to the unit price of photovoltaic modules. This refers to the unit price of the battery. This refers to the unit price of the charging station. This represents the maintenance cost coefficient for photovoltaic modules. This represents the maintenance cost coefficient for the battery. This represents the maintenance cost coefficient for charging stations. For photovoltaic modules Power generation at all times For charging piles Power demand at all times For storage batteries Power at any moment For the lifespan of photovoltaic modules and charging piles, For the lifespan of the storage battery;

[0023] As a further preferred embodiment of the present invention, the formula for calculating the equivalent life cost of the battery in step S2 is as follows: (3)

[0024] In formula (3), For the lifespan of the storage battery, For the battery within one year One charge-discharge cycle, This refers to the depth of discharge of the battery. The depth of discharge of the battery is The equivalent cycle life of the battery;

[0025] As a further preferred embodiment of the present invention, the constraints of the energy input-output integrated model in step S2 include the load shortage rate and system energy curtailment rate meeting the upper limit requirements, and the battery capacity meeting the upper and lower limit requirements, wherein the load shortage rate satisfies:

[0026] The system's energy curtailment rate satisfies:

[0027]

[0028] This represents the upper limit of the system load power shortage rate. This represents the upper limit of the system's energy curtailment rate. For the number of photovoltaic modules, The number of charging stations, For the number of batteries, For photovoltaic modules Power generation at all times For charging piles Power demand at all times For storage batteries Charging power at any time For storage batteries Discharge power at any given moment;

[0029] Battery capacity meets:

[0030]

[0031] This represents the minimum state of charge of the battery. This represents the battery's maximum state of charge. for The percentage of battery charge relative to total capacity at any given time;

[0032] As a further preferred embodiment of the present invention, in step S3, which constructs the adaptive inertia weight particle swarm algorithm, the inertia weight update strategy of the particle swarm algorithm is as follows:

[0033] (4)

[0034] In formula (4), For the first The distance between the particle and the current optimal solution at the next iteration. and Represents the coefficient of the constant term. Represented as:

[0035] (5)

[0036] In formula (5), , The maximum value of the particle's position. This represents the minimum value of the particle's position;

[0037] As a further preferred embodiment of the present invention, the specific configuration method in step S4 includes:

[0038] Step S41: After setting the population size, number of iterations, inertia weight, and learning factor, generate an initial population according to the constraints.

[0039] Step S42: Import meteorological data and calculate photovoltaic output according to the energy input-output integrated model;

[0040] Step S43: Import the annual power demand of the charging pile load;

[0041] Step S44: Calculate the battery charging and discharging power based on the battery charging and discharging model; if the photovoltaic module power generation is less than the charging pile's power load demand, and the battery capacity is greater than the battery's minimum allowable capacity, then the difference between the photovoltaic power generation and the power demand is... and The battery discharges until the photovoltaic module's power generation is less than the charging pile's power load demand, and the battery's charge is less than the battery's minimum allowable charge. The battery stops discharging;

[0042] If the photovoltaic module's power generation exceeds the charging pile's power load demand, and the battery's capacity is less than the battery's maximum allowable capacity, then the difference between the photovoltaic power generation and the power demand is... and This continues until the photovoltaic module's power generation exceeds the charging pile's power load demand, and the battery's capacity exceeds its maximum allowable capacity. The battery stops charging;

[0043] Step S45: Confirm the annual power demand of charging piles imported in step S43, until all data calculations are completed, a new population is generated, and the number of iterations is greater than or equal to a set value, then the configuration is complete; if the number of iterations is less than or equal to a set value, then the configuration is complete. If the value is set, return to step S44 and continue. +1 optimization;

[0044] As a further preferred embodiment of the present invention, the difference between the photovoltaic power generation and the power demand in step S44... Then, the battery discharges, depending on its charge level. Has it been achieved? There are two scenarios: when the battery has sufficient energy, i.e. The battery provides all the electrical energy required by the charging station; when the battery energy is insufficient, a load shortage occurs.

[0045] As a further preferred embodiment of the present invention, in step S44, the difference between the photovoltaic power generation and the power demand is... The battery is charged according to its charge level. Has it been achieved? There are two scenarios: when the battery stores all its electrical energy, that is... When the battery is fully charged and still has residual power, the phenomenon of light curtailment occurs.

[0046] By employing the above technical solutions, the present invention has the following beneficial effects compared to the prior art:

[0047] 1. The optimization configuration method for highway photovoltaic sound barrier energy storage and charging system based on improved particle swarm optimization algorithm provided by this invention takes into account the quantity and capacity configuration of photovoltaic modules, batteries and charging piles, and also considers the equivalent life cost of batteries. It has certain reference value for the capacity configuration and economy of off-grid "photovoltaic energy storage and charging" system.

[0048] 2. The optimized configuration method for highway photovoltaic sound barrier energy storage and charging system based on improved particle swarm optimization algorithm provided by this invention takes into account the actual operating conditions of each device in the system. The cost is based on the dynamic changes in the power consumption of each device. Compared with the traditional operation and maintenance cost that considers a fixed lifespan, it has more practical engineering significance.

[0049] 3. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm provided by this invention dynamically updates the inertial weights according to the distance between the current solution and the optimal solution of the particles, which effectively enhances the global search capability and local search capability of the particles and significantly improves the convergence performance of the algorithm. Attached Figure Description

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0051] Figure 1 This is a system structure diagram of the integrated energy input and output model provided by the present invention;

[0052] Figure 2 This is a flowchart of the optimized configuration method for a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm provided by the present invention.

[0053] Figure 3 This is a battery discharge flowchart provided by the present invention;

[0054] Figure 4 This is a battery charging flowchart provided by the present invention. Detailed Implementation

[0055] The invention will now be described in further detail with reference to the accompanying drawings.

[0056] Current research on optimizing photovoltaic energy storage capacity configuration largely only provides capacity configuration schemes, failing to intuitively analyze system reliability and energy spillover phenomena. Furthermore, current research focuses on optimizing photovoltaic and energy storage devices based on given electricity loads, leaving a gap in how to optimize load and energy storage capacity configuration under given photovoltaic output. Therefore, this application provides an optimized configuration method for a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm. The method uses the sum of initial system investment and equipment maintenance costs as the objective function, while considering constraints and evaluation indicators such as system power supply reliability, energy spillover ratio, and battery state of charge. It quantitatively analyzes system reliability, then predicts battery life based on a cumulative damage model and performs energy storage life feedback correction, making the investment cost model more engineering-oriented. Finally, it uses an adaptive particle swarm optimization algorithm to study the optimal capacity configuration scheme for batteries and charging piles under given photovoltaic output.

[0057] Specifically, the following steps are included:

[0058] Step S1: Establish a comprehensive energy input and output model for the highway photovoltaic sound barrier energy storage and charging system, which includes a mathematical model of photovoltaic power generation, a mathematical model of energy storage battery, and charging piles located on the side of the highway to provide power to electric bicycles;

[0059] like Figure 1 As shown, the photovoltaic (PV) power generation mathematical model includes several PV modules. In the road traffic sector, combining the PV power generation system with a sound barrier noise reduction system achieves effective noise reduction while fully utilizing clean solar energy, converting light energy into electrical energy as the input source for the highway PV sound barrier charging system. The energy storage battery mathematical model includes an energy storage system composed of several batteries, which can achieve bidirectional energy flow and features smoothing power fluctuations and fast response speed. It can effectively complement the PV power generation mathematical model, while improving the system's power supply stability. The charging station for electric bicycles directly obtains clean solar energy from the batteries, replacing the previous mode of drawing power from the power grid.

[0060] Step S2: Determine the number of photovoltaic modules in the photovoltaic power generation mathematical model, the number of batteries and charging piles in the energy storage battery mathematical model, construct the objective function of the energy input-output integrated model based on the equivalent life cost of the battery, and set the constraints of the energy input-output integrated model.

[0061] The objective function includes the photovoltaic output of the photovoltaic module. The main environmental factors affecting photovoltaic power generation include irradiance and ambient temperature. The calculation formula is as follows:

[0062]

[0063] In formula (1), This represents the real-time power generation of the photovoltaic modules. ; Solar radiation intensity, ; The operating temperature of the battery. ; For ambient reference temperature, 25 ; This indicates the maximum output power of the photovoltaic module under standard test conditions. ; Indicates the light intensity under standard conditions. ; The power-temperature conversion factor, / .

[0064] The number of batteries and charging stations in the objective function satisfies the following combination:

[0065] ,

[0066] ,

[0067] (2)

[0068] In formula (2), For the number of photovoltaic modules, The number of charging stations, For the number of batteries, This refers to the unit price of photovoltaic modules. This refers to the unit price of the battery. This refers to the unit price of the charging station. This represents the maintenance cost coefficient for photovoltaic modules. This represents the maintenance cost coefficient for the battery. This represents the maintenance cost coefficient for charging stations. For photovoltaic modules Power generation at all times For charging piles Power demand at all times For storage batteries Power at any moment For the lifespan of photovoltaic modules and charging piles, This refers to the lifespan of the battery.

[0069] The equivalent life cost of the battery adopts the equivalent cycle life method. Based on photovoltaic output and electricity consumption, the battery charge curve is obtained, and the rainflow counting method is used for data compression and cycle number extraction. Then, the equivalent life is calculated according to the equivalent cycle life method. The specific calculation formula is as follows: (3)

[0070] In formula (3), For the lifespan of the storage battery, For the battery within one year One charge-discharge cycle, This refers to the depth of discharge of the battery. The depth of discharge of the battery is The equivalent cycle life of the battery.

[0071] The integrated energy input and output model also includes corresponding constraints. Specific constraints include ensuring that the load shortage rate and system energy curtailment rate meet upper limits, and that the battery capacity meets upper and lower limits. The load shortage rate, in particular, satisfies the following:

[0072] The system's energy curtailment rate satisfies:

[0073]

[0074] This represents the upper limit of the system load power shortage rate. This represents the upper limit of the system's energy curtailment rate. For the number of photovoltaic modules, The number of charging stations, For the number of batteries, For photovoltaic modules Power generation at all times For charging piles Power demand at all times For storage batteries Charging power at any time For storage batteries Discharge power at any given moment;

[0075] Battery capacity meets:

[0076]

[0077] This represents the minimum state of charge of the battery. This represents the battery's maximum state of charge. for The percentage of battery charge relative to total capacity at any given time.

[0078] Step S3: Based on the distance between the particle's position and the optimal position, dynamically adjust the inertia weight to construct an adaptive inertia weight particle swarm algorithm; the inertia weight update strategy of the particle swarm algorithm is as follows:

[0079] (4)

[0080] In formula (4), For the first The distance between the particle and the current optimal solution at the next iteration. and Represents the coefficient of the constant term. Represented as:

[0081] (5)

[0082] In formula (5), , The maximum value of the particle's position. This represents the minimum value of the particle's position.

[0083] Step S4: Under the objective function of the energy input-output integrated model and the set constraints, the configuration of the highway photovoltaic sound barrier energy storage and charging system is solved using the constructed adaptive inertial weighted particle swarm algorithm to obtain the optimal equipment capacity configuration.

[0084] Specific configuration methods include:

[0085] Step S41: After setting the population size, number of iterations, inertia weight, and learning factor, generate an initial population according to the constraints.

[0086] Step S42: Import meteorological data and calculate photovoltaic output according to the energy input-output integrated model;

[0087] Step S43: Import the annual power demand of the charging pile load;

[0088] Step S44: Calculate the battery charging and discharging power based on the battery charging and discharging model; if the photovoltaic module power generation is less than the charging pile's power load demand, and the battery capacity is greater than the battery's minimum allowable capacity, then the difference between the photovoltaic power generation and the power demand is... and The battery discharges until the photovoltaic module's power generation is less than the charging pile's power load demand, and the battery's charge is less than the battery's minimum allowable charge. The battery stops discharging;

[0089] If the photovoltaic module's power generation exceeds the charging pile's power load demand, and the battery's capacity is less than the battery's maximum allowable capacity, then the difference between the photovoltaic power generation and the power demand is... and This continues until the photovoltaic module's power generation exceeds the charging pile's power load demand, and the battery's capacity exceeds its maximum allowable capacity. The battery stops charging;

[0090] As mentioned above, a battery has two states: a discharging state and a charging state. Figure 3 The diagram shown represents the discharge process, specifically the difference between photovoltaic power generation and electricity demand. Then, the battery discharges, depending on its charge level. Has it been achieved? There are two scenarios: when the battery has sufficient energy, i.e. The battery provides all the electrical energy required by the charging station; when the battery energy is insufficient, a load shortage occurs.

[0091] Figure 4 The diagram shown is a charging flowchart, which represents the difference between the photovoltaic power generation and the power demand. The battery is charged according to its charge level. Has it been achieved? There are two scenarios: when the battery stores all its electrical energy, that is... When the battery is fully charged and still has residual power, the phenomenon of light curtailment occurs.

[0092] Step S45: Confirm the annual power demand of charging piles imported in step S43, until all data calculations are completed, a new population is generated, and the number of iterations is greater than or equal to a set value, then the configuration is complete; if the number of iterations is less than or equal to a set value, then the configuration is complete. If the value is set, return to step S44 and continue. +1 optimization.

[0093] In a preferred embodiment, Figure 2 The optimization process shown imports the annual power demand of the charging pile load and calculates the annual photovoltaic power generation curve based on the solar resource data of the test site, which consists of the hourly power generation of the photovoltaic modules, totaling 8760 data points. During the optimization process, a new population is generated after all data is completed, and the configuration is completed after the number of iterations is greater than or equal to a set value.

[0094] The above-mentioned optimized configuration method for highway photovoltaic sound barrier charging and storage systems based on the improved particle swarm optimization algorithm achieves the lowest comprehensive index of investment cost, load shortage rate, and system curtailment rate for a given photovoltaic module by providing a combination of charging pile and battery capacity configurations that meet its power generation requirements.

[0095] In summary, this application, by improving the particle swarm optimization algorithm and considering the battery's lifespan based on its charging and discharging power, can quickly and accurately solve for the optimal capacity configuration scheme of the photovoltaic-storage-charging system, which is more in line with actual conditions and has stronger engineering feasibility.

[0096] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0097] The meaning of "and / or" as used in this application includes situations where each exists alone or both exist simultaneously.

[0098] The term "connection" as used in this application can mean a direct connection between components or an indirect connection between components through other components.

[0099] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A highway photovoltaic sound barrier storage and charging system optimization configuration method based on an improved particle swarm algorithm, characterized in that: Specifically, the following steps are included: Step S1: Establish a comprehensive energy input and output model for the highway photovoltaic sound barrier energy storage and charging system, which includes a mathematical model of photovoltaic power generation, a mathematical model of energy storage battery, and charging piles located on the side of the highway to provide power to electric bicycles; Step S2: Determine the number of photovoltaic modules in the photovoltaic power generation mathematical model, the number of batteries and charging piles in the energy storage battery mathematical model, construct the objective function of the energy input and output integrated model based on the equivalent life cost of the battery, and set the constraints of the energy input and output integrated model. Step S3: Based on the distance between the particle's position and the optimal position, dynamically adjust the inertia weight to construct an adaptive inertia weight particle swarm algorithm; the inertia weight update strategy of the particle swarm algorithm is as follows: ， wherein is the first distance of the particle at the nth iteration from the current best solution, and denotes the constant term coefficient, denotes ， wherein , is a maximum value of the particle position, is a minimum value of the particle position; Step S4: Under the objective function of the energy input-output integrated model and the set constraints, the configuration of the highway photovoltaic sound barrier energy storage and charging system is solved using the constructed adaptive inertial weighted particle swarm algorithm to obtain the optimal equipment capacity configuration.

2. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm as described in claim 1, characterized in that: In the integrated energy input and output model established in step S1, the photovoltaic modules in the photovoltaic power generation mathematical model utilize clean solar energy to convert light energy into electrical energy, which serves as the input source for the highway photovoltaic sound barrier energy storage and charging system. The mathematical model of the energy storage battery shows that the battery, as an energy storage device, can realize bidirectional energy flow; Electric bicycle charging stations draw clean solar energy directly from the batteries.

3. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm as described in claim 1, characterized in that: The objective function in step S2 includes the photovoltaic output of the photovoltaic module, and the calculation formula is as follows: ， In the formula, This represents the real-time power generation of the photovoltaic modules. ; Solar radiation intensity, ; The operating temperature of the battery. ; For ambient reference temperature, 25 ; This indicates the maximum output power of the photovoltaic module under standard test conditions. ; Indicates the light intensity under standard conditions. ; The power-temperature conversion factor, / .

4. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm as described in claim 1, characterized in that: In step S2, the number of batteries and charging piles in the objective function satisfies the following combination: ， ， ， In the formula, For the number of photovoltaic modules, The number of charging stations, The number of batteries, This refers to the unit price of photovoltaic modules. This refers to the unit price of the battery. This refers to the unit price of the charging station. This represents the maintenance cost coefficient for photovoltaic modules. This represents the maintenance cost coefficient for the battery. This represents the maintenance cost coefficient for charging stations. For photovoltaic modules Power generation at all times For charging piles Power demand at all times For storage batteries Power at any moment For the lifespan of photovoltaic modules and charging piles, This refers to the lifespan of the battery.

5. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm according to claim 1, characterized in that: The formula for calculating the equivalent life cost of the battery in step S2 is as follows: , In the formula, For the lifespan of the storage battery, For the battery within one year One charge-discharge cycle, This refers to the depth of discharge of the battery. The depth of discharge of the battery is The equivalent cycle life of the battery.

6. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm according to claim 1, characterized in that: The constraints of the energy input-output integrated model in step S2 include the load shortage rate and system energy curtailment rate meeting the upper limit requirements, and the battery capacity meeting the upper and lower limit requirements. Among them, the load shortage rate satisfies: , The system's energy curtailment rate satisfies: ， This represents the upper limit of the system load power shortage rate. This represents the upper limit of the system's energy curtailment rate. For the number of photovoltaic modules, The number of charging stations, The number of batteries, For photovoltaic modules Power generation at all times For charging piles Power demand at all times For storage batteries Charging power at any time For storage batteries Discharge power at any given moment; Battery capacity meets: ， This represents the minimum state of charge of the battery. This represents the battery's maximum state of charge. for The percentage of battery charge relative to total capacity at any given time.

7. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm according to claim 1, characterized in that: In step S4, the specific configuration method includes: Step S41: After setting the population size, number of iterations, inertia weight, and learning factor, generate an initial population according to the constraints. Step S42: Import meteorological data and calculate photovoltaic output according to the energy input-output integrated model; Step S43: Import the annual power demand of the charging pile load; Step S44: Calculate the battery charging and discharging power based on the battery charging and discharging model; if the photovoltaic module power generation is less than the charging pile's power load demand, and the battery capacity is greater than the battery's minimum allowable capacity, then the difference between the photovoltaic power generation and the power demand is... and The battery discharges until the photovoltaic module's power generation is less than the charging pile's power load demand, and the battery's charge is less than the battery's minimum allowable charge. The battery stops discharging; If the photovoltaic module's power generation exceeds the charging pile's power load demand, and the battery's capacity is less than the battery's maximum allowable capacity, then the difference between the photovoltaic power generation and the power demand is... and This continues until the photovoltaic module's power generation exceeds the charging pile's power load demand, and the battery's capacity exceeds its maximum allowable capacity. The battery stops charging; Step S45: Confirm the annual power demand of charging piles imported in step S43, until all data calculations are completed, a new population is generated, and the number of iterations is greater than or equal to a set value, then the configuration is complete; if the number of iterations is less than or equal to a set value, then the configuration is complete. If the value is set, return to step S44 and continue. +1 optimization.

8. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm according to claim 7, characterized in that: The difference between photovoltaic power generation and electricity demand in step S44 Then, the battery discharges, depending on its charge level. Has it been achieved? There are two scenarios: when the battery has sufficient energy, i.e. The battery provides all the electrical energy required by the charging station; when the battery energy is insufficient, a load shortage occurs.

9. The method for optimizing the configuration of a highway photovoltaic sound barrier energy storage and charging system based on an improved particle swarm optimization algorithm as described in claim 8, characterized in that: After the difference between photovoltaic power generation and electricity demand in step S44 The battery is charged according to its charge level. Has it been achieved? There are two scenarios: when the battery stores all its electrical energy, that is... When the battery is fully charged and still has residual power, the phenomenon of light curtailment occurs.

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