Electric forklift-considered green storage optical storage system optimization scheduling method

Abstract

The invention belongs to the technical field of electric power systems, and particularly relates to a green storage optical storage system optimization scheduling method considering an electric forklift, comprising the following steps: acquiring photovoltaic data and energy storage data; constructing a green storage light storage scheduling model based on the photovoltaic data and the energy storage data; by expanding a search space, optimizing a local search strategy and optimizing a global search strategy, an improved salmons swarm optimization algorithm is obtained; and optimizing the green storage light storage scheduling model by using an improved salmons swarm optimization algorithm to obtain an optimal scheduling result. According to the invention, by introducing multi-strategy improvements such as chaotic mapping, an adaptive particle optimization strategy, a dynamic time factor, polynomial variation and the like, the soloat group method is improved. According to the improvement, the energy utilization efficiency is improved, the micro-grid system can more effectively utilize renewable energy sources in green storage, and the power demand is met to the maximum extent.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and in particular relates to an optimized scheduling method for a green warehousing photovoltaic-storage system that takes into account electric forklifts. Background Technology

[0002] Against the backdrop of the current global energy crisis and environmental pollution, the utilization of green energy and environmental protection have become a global focus. As one solution, microgrid technology has gained attention due to its ability to flexibly integrate renewable energy and improve energy efficiency. Furthermore, with the promotion of sustainable development concepts, the concept of green warehousing has emerged, aiming to reduce the environmental impact of warehouse operations and improve energy efficiency through measures such as adopting environmentally friendly materials, optimizing energy use, and reducing waste generation.

[0003] Under this trend, electric forklifts, as an important component of green warehousing, have become an ideal alternative to traditional fuel-powered forklifts due to their low emissions, low noise, and high energy efficiency. However, to maximize their environmental and economic benefits, efficient management and scheduling of electric forklifts are crucial. This involves not only the charging strategy and energy management of electric forklifts but also their coordination with warehousing needs and microgrid energy supply. Therefore, developing an optimized scheduling method for electric forklifts in green warehousing microgrids is particularly important. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an optimized scheduling method for a green warehousing photovoltaic-storage system that considers electric forklifts. The aim is to achieve efficient, environmentally friendly, and economical operation of warehousing and logistics by improving the utilization efficiency of electric forklifts and the energy utilization efficiency of microgrids.

[0005] To achieve the above objectives, the present invention provides an optimized scheduling method for a green warehousing and energy storage system that considers electric forklifts, comprising:

[0006] Acquire photovoltaic and energy storage data;

[0007] Based on the photovoltaic and energy storage data, a green warehousing photovoltaic-energy storage scheduling model is constructed.

[0008] An improved sand cat swarm optimization algorithm is obtained by expanding the search space, optimizing the local search strategy, and optimizing the global search strategy.

[0009] The green warehouse photovoltaic-storage scheduling model was optimized using an improved sand cat swarm optimization algorithm to obtain the optimal scheduling result.

[0010] Optionally, the green warehousing photovoltaic-storage scheduling model includes: photovoltaic modules, battery energy storage modules, and green warehousing modules;

[0011] The photovoltaic module is used to obtain the photovoltaic cell conversion power based on the photovoltaic data;

[0012] The battery energy storage module is used to obtain the state of charge of the battery based on the energy storage data.

[0013] The green warehousing module is used to obtain the expected power consumption of electric forklift users.

[0014] Optionally, the method for obtaining the photovoltaic cell conversion power is as follows:

[0015] ;

[0016] in, For photovoltaic cell power conversion output, Indicates the actual light intensity; This represents the temperature of the photovoltaic panel at time t; This indicates the output power of the photovoltaic panel; Indicates the light intensity under standard conditions; This indicates the maximum output power under standard conditions; Indicates the reference temperature of the photovoltaic cell; This represents the power temperature coefficient.

[0017] Optionally, obtaining the state of charge of the battery includes:

[0018] ;

[0019] in, The state of charge of the energy storage battery at time t; Let t be the charging power of the energy storage battery at time t; Let t be the discharge power of the energy storage battery at time t; This refers to the battery's rated capacity. Unit of time; and These represent the charging / discharging efficiency and self-discharge efficiency of the energy storage battery, respectively.

[0020] Optionally, obtaining the electric forklift user's expected battery level includes:

[0021] ;

[0022] in, L represents the electric forklift user's desired battery capacity, and L represents the daily driving range of the EV. , Let be the expected value and the expected standard deviation.

[0023] Optionally, the green warehouse photovoltaic-storage scheduling model can be optimized using an improved sand cat swarm optimization algorithm to obtain the optimal scheduling result, including:

[0024] An initial population is obtained, and the Logistic-Tent chaotic mapping method is introduced to initialize the initial population to obtain the initialized population;

[0025] Calculate the fitness value of each individual in the initial population, and determine the initial global best individual and the fitness value corresponding to the initial global best individual;

[0026] Based on the initial global optimal individual and its corresponding fitness value, iterative optimization is performed. An adaptive particle optimization strategy and a dynamic time factor update strategy are used to update the position and velocity of each individual. The fitness value of each individual after the update is calculated, and the historical best position and global optimal individual of each individual are updated. If the random number is less than the set mutation probability, a polynomial mutation is performed on the global optimal individual to generate a new individual, and its fitness value is calculated. The fitness value of the new individual is compared with the fitness value corresponding to the initial global optimal individual to obtain the comparison result.

[0027] Based on the comparison results, determine whether the current iteration count has reached the maximum iteration count; if it has, then the scheduling scheme corresponding to the current globally optimal individual is taken as the optimal scheduling result; otherwise, continue iterating.

[0028] Optionally, initializing the initial population includes: introducing the Logistic-Tent chaotic mapping method to obtain the initial population;

[0029] The Logistic-Tent chaotic mapping method is as follows:

[0030] ;

[0031] Where n is the number of iterations. For control parameters, For the first The state variables of the step, For the first The state variable of the step, r is the control parameter.

[0032] Optionally, the dynamic time factor update strategy is as follows:

[0033] ;

[0034] Where μ is the contraction factor; Let be the velocity of particle i in the kth iteration; Let i be the velocity of particle i in the (k+1)th iteration. Let i be the position of particle i in the k-th iteration; Let i be the position of particle i in the (k+1)th iteration. Let be the local optimal position of particle i in the k-th iteration; Let be the globally optimal position of particle i in the k-th iteration; and Let K be two uniformly distributed random numbers in the range [0, 1]; K is the maximum number of iterations. A random number in the range [0.5, 1]. For dynamic nonlinear time factors, This refers to the interaction force between particles.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] This invention improves the sand cat swarm optimization method by introducing multiple strategies, including chaotic mapping, adaptive particle optimization, dynamic time factor, and polynomial mutation. This improvement enhances energy efficiency, enabling microgrid systems to more effectively utilize renewable energy from green storage and maximize electricity demand satisfaction.

[0037] The optimized scheduling algorithm of this invention reduces operating costs, responds more flexibly to changes in electricity demand and market prices, and enables the system to operate economically, especially in the management of energy storage units.

[0038] The robustness enhancement strategy introduced in this invention improves the system's adaptability to changes in the external environment and load fluctuations, thereby enhancing the stability of the microgrid system.

[0039] This invention reduces reliance on traditional energy sources and lowers the carbon footprint by more accurately predicting and optimizing energy use, thus further mitigating environmental impact. Improvements enhance system stability, strengthen global and local search capabilities, and consequently improve the overall performance of the microgrid dispatch system. Attached Figure Description

[0040] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a flowchart of an optimized scheduling method for a green warehousing and energy storage system considering electric forklifts, according to an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of chaotic sequences according to an embodiment of the present invention, wherein (a) is a chaotic sequence distribution diagram and (b) is a chaotic sequence histogram;

[0043] Figure 3 This is an optimized flowchart of an embodiment of the present invention. Detailed Implementation

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0046] This embodiment proposes an optimized scheduling method for a green warehousing and energy storage system that considers electric forklifts, such as... Figure 1 As shown, the specific steps include:

[0047] Acquire photovoltaic and energy storage data;

[0048] Based on photovoltaic and energy storage data, a green warehousing photovoltaic-energy storage scheduling model is constructed.

[0049] An improved sand cat swarm optimization algorithm is obtained by expanding the search space, optimizing the local search strategy, and optimizing the global search strategy.

[0050] An improved sand cat swarm optimization algorithm was used to optimize the green warehouse photovoltaic storage scheduling model to obtain the optimal scheduling result.

[0051] Specifically, step one: Complementarily combine photovoltaic units and energy storage units to build a photovoltaic-storage combined power generation system model and scheduling model;

[0052] Step 2: Investigate the electricity load and photovoltaic output characteristics of green warehousing including electric forklifts;

[0053] Step 3: Improve the Sand Cat Group Optimization (SCSO) algorithm by incorporating Henon-Logistic chaotic mapping and nonlinear adjustment mechanisms;

[0054] Step 4: Apply it to solve the microgrid optimization problem involving electric forklifts.

[0055] Furthermore, the green warehousing photovoltaic-storage scheduling model includes: photovoltaic modules, battery energy storage modules, and green warehousing modules;

[0056] Photovoltaic modules are used to obtain the conversion power of photovoltaic cells based on photovoltaic data;

[0057] A battery energy storage module is used to obtain the state of charge of the battery based on energy storage data.

[0058] The green warehousing module is used to obtain the expected power consumption of electric forklift users.

[0059] Specifically, for photovoltaic (PV) units, both illumination and PV cell models need to be considered; for energy storage units, energy storage conversion models and charge / discharge control are required. The entire system needs coordinated control strategies and scheduling algorithms to adapt to grid load demands and environmental conditions. An intelligent control system is used to monitor and adjust the system's operating status in real time, ensuring system stability and reliability. Such a system model can effectively improve energy efficiency, reduce costs, and minimize environmental impact.

[0060] The mathematical model for the power output conversion of a photovoltaic cell can be expressed as:

[0061] ;

[0062] in, For photovoltaic cell power conversion output, Indicates the actual light intensity; This represents the temperature of the photovoltaic panel at time t; This indicates the output power of the photovoltaic panel; Indicates the light intensity under standard conditions; This indicates the maximum output power under standard conditions; Indicates the reference temperature of the photovoltaic cell; This represents the power temperature coefficient.

[0063] Batteries play a vital role in microgrids as energy storage devices. They can supplement demand when other sources of power are insufficient and store excess electricity for backup when the load is low, thus playing a role in peak shaving and valley filling. Batteries satisfy the following formula during charging and discharging.

[0064] ;

[0065] in, The state of charge of the energy storage battery at time t; Let t be the charging power of the energy storage battery at time t; Let t be the discharge power of the energy storage battery at time t; This refers to the battery's rated capacity. Unit of time; and These represent the charging / discharging efficiency and self-discharge efficiency of the energy storage battery, respectively.

[0066] The driving characteristics of electric forklifts have a significant impact on their operating schedules and charging / discharging times. Electric forklifts come in various types and have diverse applications; in green warehousing, their primary use is for transporting goods. Based on extensive statistical data, the probability density function of the moment an EV begins charging follows a normal distribution, as shown in the following equation:

[0067] ;

[0068] In the formula, Let be the probability density function of a cyclic normal distribution, and μ and σ be the mean and standard deviation of the normal distribution, respectively.

[0069] According to statistical data, the expected power consumption of electric forklift users follows a log-normal distribution as shown in the following formula:

[0070] ;

[0071] In the formula: L is the daily driving distance of the EV; , These are the expected mean and expected standard deviation;

[0072] The charging time for an electric forklift can be determined by the daily mileage, expressed as follows:

[0073] ;

[0074] In the formula, The charging time required for an electric forklift. The power consumption of an electric forklift per 100 kilometers. Power for charging electric forklifts Efficiency of charging electric forklifts.

[0075] More specifically, green warehousing has a series of unique characteristics in its electricity consumption. First, green warehousing tends to use renewable energy sources, such as solar power, to meet its electricity needs. This makes its power system more environmentally friendly and reduces reliance on traditional energy sources. Second, green warehousing is often equipped with advanced energy-saving equipment and technologies to minimize energy waste. This includes the use of high-efficiency lighting systems, intelligent temperature control equipment, and energy-saving drive systems. Additionally, green warehousing tends to implement energy management systems to further improve energy efficiency by monitoring and optimizing electricity consumption behavior. Overall, the electricity consumption characteristics of green warehousing emphasize sustainability and energy conservation, aiming to reduce dependence on traditional energy sources, mitigate environmental impact, and meet the electricity needs of warehousing operations.

[0076] The operating patterns of electric forklifts in green warehousing are relatively fixed, requiring two power replenishments daily. To meet the daily operational needs of the electric forklifts, fast charging is used during the daytime (10:00 AM to 4:00 PM) due to the short charging time. Charging takes place at night when electricity prices are low. It is assumed that the initial charging time and initial battery state of charge (SOC) follow a normal distribution during the daytime charging period. From 11:00 PM to 3:00 AM, considering the power grid and charging costs, conventional charging is used for power replenishment, with forklifts starting to charge around 11:00 PM. Therefore, assuming the initial charging time of the electric forklifts follows a uniform distribution between 11:00 PM and midnight, and the initial SOC follows a normal distribution, a fast-charging load model for the electric forklifts is established. The specific description is shown in Table 1.

[0077] Table 1

[0078] Charging times Charging time period Start of charging time Starting SOC Charging method Charging probability 1 10:00-16:00 N(13,0.5^2) N(0.5，0.1^2) Fast charging 1 1 23:00-3:00 the next day U(23,24) N(0.5，0.1^2) Fast charging 1

[0079] Furthermore, the improved sand cat swarm optimization algorithm is used to optimize the green warehouse photovoltaic-storage scheduling model, and the optimal scheduling results are obtained, including:

[0080] Obtain the initial population and introduce the Logistic-Tent chaotic mapping method to initialize the initial population;

[0081] Calculate the fitness value of each individual in the initial population, and determine the initial global best individual and the fitness value corresponding to the initial global best individual;

[0082] Based on the initial global best individual and its corresponding fitness value, iterative optimization is performed. An adaptive particle optimization strategy and a dynamic time factor update strategy are used to update the position and velocity of each individual. The fitness value of each individual after the update is calculated, and the historical best position and global best individual of each individual are updated. If the random number is less than the set mutation probability, a polynomial mutation is performed on the global best individual to generate a new individual, and its fitness value is calculated. The fitness value of the new individual is compared with the fitness value corresponding to the initial global best individual to obtain the comparison result.

[0083] Based on the comparison results, determine whether the current iteration count has reached the maximum iteration count; if it has, then the scheduling scheme corresponding to the current globally optimal individual is taken as the optimal scheduling result; otherwise, continue iterating.

[0084] Specifically, the sand cat swarm optimization method randomly generates the initial population, resulting in a low-quality population that negatively impacts the algorithm's convergence speed and optimization results. This embodiment introduces a Logistic-Tent chaotic mapping during population initialization to improve population quality and convergence speed. The mapping equation is as follows:

[0085] ;

[0086] In the formula: n is the number of iterations; For control parameters, take =0.1, For the first The state variables of the step, For the first The state variable of the step, r is the control parameter.

[0087] When n=2000, the Logistic-Tent chaotic sequence distribution diagram is as follows: Figure 2 As shown in (a)-(b), the results have a uniform distribution density, which can effectively enhance the convergence speed and global search capability of the algorithm.

[0088] During the PSO algorithm's solution process, the history of the search particles and the globally optimal particle are continuously updated, guiding other search particles to fly towards the optimal position, thus achieving algorithm convergence. However, the rapid aggregation of search particles can lead to many invalid solutions at close range and easily cause the algorithm to fall into local optima. Therefore, an adaptive operator is introduced to set the particle optimization strategy. When a particle satisfies the position update condition, the position transformation can be performed according to the formula to find the optimal position. The specific strategy is as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, e is the adaptive operator; rand(·) is the random number function; An adaptive threshold; Let be the Euclidean distance between particle i and its nearest neighbor, particle j, in the D-dimensional space formed by the objective function; Let be the Euclidean distance between particle i and the best particle in the population. Q is the Euclidean distance threshold; Q is the optimization decision threshold. As the initial value, This represents the current iteration number. The maximum number of iterations, Let be the position of the i-th particle at the next iteration time. Let be the vector position of the i-th particle. In the early stages of the algorithm, the initial particles and the optimal particles are far apart. When the value is relatively large, the adaptive operator e is also relatively large. The particle position update strategy is mainly affected by the adaptive operator term, and the particle diversity is enhanced. In the later stage of the algorithm, the particles gradually approach the global optimum. At this time, the adaptive operator term has a smaller impact on the particle position update. The particles mainly rely on their own positions to find the optimal solution, ensuring the algorithm's ability to solve problems in a small range.

[0093] Because the original PSO algorithm causes particles to oscillate around the optimal solution during particle position updates, making it difficult to find the optimal solution, this embodiment introduces a dynamic nonlinear time factor. To solve this problem, the search range is increased in the early stages of the algorithm, and later... The reduction in size enhances local search capabilities:

[0094] ;

[0095] Where μ is the contraction factor; Let be the velocity of particle i in the kth iteration; Let i be the velocity of particle i in the (k+1)th iteration. Let i be the position of particle i in the k-th iteration; Let i be the position of particle i in the (k+1)th iteration. Let be the local optimal position of particle i in the k-th iteration; Let be the globally optimal position of particle i in the k-th iteration; and Let K be two uniformly distributed random numbers in the range [0, 1]; K is the maximum number of iterations. A random number in the range [0.5, 1]. For dynamic nonlinear time factors, This refers to the interaction force between particles.

[0096] ;

[0097] In the formula: τ is the acceleration coefficient.

[0098] τ controls the algorithm's exploration behavior, helping to avoid early convergence and prevent the algorithm from getting trapped in local optima during the optimization process. Fine-tuning this parameter can improve global search capabilities, that is:

[0099] ;

[0100] ;

[0101] In the formula: These are the initial velocity and dependent velocity that enable particle motion, respectively. The force between particles The particle velocity updates are controlled, and a balance is struck between the global and local searches in the algorithm. This aims to provide reasonable exploration capability and accurate solutions. It is variable during the iteration process.

[0102] As an optional implementation, when the random number Rand is less than the set mutation probability Pro, a polynomial mutation is performed on the best individual to find the optimal fitness value. The new fitness value is calculated and compared with the previous fitness value. If the fitness value corresponding to the current iteration is better than the historical fitness value, the fitness value is updated. The best position in the entire population is recorded; if the current fitness value of a particle is better than the global historical fitness value, it is updated. When the number of iterations is maximized, the output result is the optimal scheduling result.

[0103] Mutation operators play a crucial role in the optimization process of intelligent algorithms, influencing their optimization performance. Multinomial mutation uses a multinomial probability distribution to transform the current value of a continuous variable into its nearest neighboring value, thereby more accurately searching for the nearest optimal solution. Therefore, to further improve the algorithm's global search capability and solution diversity, a multinomial mutation operator is introduced to expand the search range, giving it a certain degree of local random search capability.

[0104] The polynomial mutation operator has the following form:

[0105] ;

[0106] ;

[0107] In the formula: The optimal individual position before mutation; The optimal position for the individual after mutation; , These are the upper and lower bounds of the position, respectively; u is a random number between [0, 1]. q is the distribution index; q is the mutation operator. q1 is the first mutation operator, and q2 is the second mutation operator.

[0108] The improved sand cat group method flowchart is as follows: Figure 3 As shown, the introduction of chaotic mapping expands the search space, which helps in finding the global optimum. By introducing an adaptive particle optimization strategy, this invention optimizes the algorithm's local search strategy, making it more adaptable to the complexity and dynamic changes of the problem, thus improving its local search capability. The introduction of a dynamic time factor allows the algorithm to adjust its learning rate and convergence speed according to the characteristics of the problem and the solution process, improving its robustness. The introduction of polynomial mutation increases the algorithm's diversity, prevents it from getting trapped in local optima, and improves its convergence and global search performance when dealing with multi-objective, multi-constraint nonlinear problems.

[0109] This embodiment focuses on green warehousing and improves the sand cat swarm optimization method by introducing multiple strategies, including chaotic mapping, adaptive particle optimization, dynamic time factor, and polynomial mutation. This improvement enhances energy efficiency, enabling microgrid systems to more effectively utilize renewable energy from green warehousing and maximize electricity demand.

[0110] Optimized scheduling algorithms reduce operating costs, and the system achieves economical operation by responding more flexibly to changes in electricity demand and market prices, especially in the management of energy storage units.

[0111] The introduced robustness enhancement strategy improves the system's adaptability to changes in the external environment and load fluctuations, thereby enhancing the stability of the microgrid system.

[0112] By more accurately predicting and optimizing energy use, the system reduces its reliance on traditional energy sources, lowers its carbon footprint, and further mitigates its environmental impact. Improvements have enhanced system stability, strengthened global and local search capabilities, and thus improved the overall performance of the microgrid dispatch system.

[0113] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing the scheduling of a green warehousing and energy storage system considering electric forklifts, characterized in that, include: Acquire photovoltaic and energy storage data; Based on the photovoltaic and energy storage data, a green warehousing photovoltaic-energy storage scheduling model is constructed. An improved sand cat swarm optimization algorithm is obtained by expanding the search space, optimizing the local search strategy, and optimizing the global search strategy. The green warehouse photovoltaic-storage scheduling model was optimized using an improved sand cat swarm optimization algorithm to obtain the optimal scheduling result; The green warehouse photovoltaic-storage scheduling model is optimized using an improved sand cat swarm optimization algorithm to obtain the optimal scheduling result, including: An initial population is obtained, and the Logistic-Tent chaotic mapping method is introduced to initialize the initial population to obtain the initialized population; Calculate the fitness value of each individual in the initial population, and determine the initial global best individual and the fitness value corresponding to the initial global best individual; Based on the initial global optimal individual and its corresponding fitness value, iterative optimization is performed. An adaptive particle optimization strategy and a dynamic time factor update strategy are used to update the position and velocity of each individual. The updated fitness value of each individual is calculated, and the historical best position and global optimal individual of each individual are updated. If the random number is less than the set mutation probability, a polynomial mutation is performed on the global optimal individual to generate a new individual, and its fitness value is calculated. The fitness value of the new individual is compared with the fitness value corresponding to the initial global optimal individual to obtain the comparison result. Based on the comparison results, determine whether the current iteration count has reached the maximum iteration count; if it has, then the scheduling scheme corresponding to the current globally optimal individual is taken as the optimal scheduling result; otherwise, continue iterating. Initializing the initial population includes: introducing the Logistic-Tent chaotic mapping method to obtain the initial population; The Logistic-Tent chaotic mapping method is as follows: ； Where n is the number of iterations. For control parameters, For the first The state variables of the step, For the first The state variables of the step, where r is the control parameter; The dynamic time factor update strategy is as follows: ； Where μ is the contraction factor; Let be the velocity of particle i in the kth iteration; Let i be the velocity of particle i in the (k+1)th iteration. Let i be the position of particle i in the k-th iteration; Let i be the position of particle i in the (k+1)th iteration. Let be the local optimal position of particle i in the k-th iteration; Let be the globally optimal position of particle i in the k-th iteration; and Let K be two uniformly distributed random numbers in the range [0, 1]; K is the maximum number of iterations. A random number in the range [0.5, 1]. For dynamic nonlinear time factors, This refers to the interaction force between particles.

2. The optimized scheduling method for a green warehousing and energy storage system considering electric forklifts as described in claim 1, characterized in that, The green warehousing photovoltaic energy storage scheduling model includes: photovoltaic modules, battery energy storage modules, and green warehousing modules; The photovoltaic module is used to obtain the photovoltaic cell conversion power based on the photovoltaic data; The battery energy storage module is used to obtain the state of charge of the battery based on the energy storage data. The green warehousing module is used to obtain the expected power consumption of electric forklift users.

3. The method for optimizing the scheduling of a green warehousing and energy storage system considering electric forklifts as described in claim 2, characterized in that, The method for obtaining the conversion power of the photovoltaic cell is as follows: ； in, For photovoltaic cell power conversion output, Indicates the actual light intensity. This represents the temperature of the photovoltaic panel at time t. This indicates the output power of the photovoltaic panel. Indicates the light intensity under standard conditions. This indicates the maximum output power under standard conditions. Indicates the reference temperature of the photovoltaic cell. This represents the power temperature coefficient.

4. The optimized scheduling method for a green warehousing and energy storage system considering electric forklifts according to claim 2, characterized in that, Obtaining the state of charge of the battery includes: ； in, Let t be the state of charge of the energy storage battery. Let be the charging power of the energy storage battery at time t. Let be the discharge power of the energy storage battery at time t. For the battery's rated capacity, For a unit of time, and These represent the charging / discharging efficiency and self-discharge efficiency of the energy storage battery, respectively.

5. The method for optimizing the scheduling of a green warehousing and energy storage system considering electric forklifts according to claim 2, characterized in that, The expected battery capacity of electric forklift users includes: ； in, L represents the electric forklift user's desired battery capacity, and L represents the daily driving range of the EV. , Let be the expected value and the expected standard deviation.

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