A method, device, equipment and medium for configuring light storage capacity of a greenhouse power supply system

Abstract

The present application belongs to the technical field of energy, and specifically discloses a greenhouse power supply system light storage capacity configuration method, device, equipment and medium; the present application is based on typical daily load distribution data, establishes a photovoltaic capacity configuration model and an energy storage capacity configuration model; the photovoltaic capacity configuration model is solved through the MPPT algorithm, the energy storage capacity configuration model is solved through the improved particle swarm algorithm, and the optimal solution is obtained; according to the optimal solution, the light storage capacity of the greenhouse power supply system is configured; based on the MPPT algorithm and the improved particle swarm algorithm, the optimal photovoltaic capacity configuration result and the energy storage capacity configuration result are obtained, the operation speed is fast, the reliability is high, and the problems of slow late convergence speed, premature convergence and falling into a local optimal solution of the existing algorithm itself are overcome.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy, and particularly relates to a greenhouse power supply system light storage capacity configuration method, device, equipment and medium. BACKGROUND

[0002] The greenhouse system has huge energy consumption, if direct grid power is used as the energy source, huge operation cost will be formed, the economic benefit of the greenhouse will be greatly reduced, and in an extreme case, the situation of insufficient income will even occur. From the perspective of greenhouse benefit and low-carbon environmental protection, the light storage system with appropriate capacity is an effective way to solve the above problems.

[0003] The greenhouse load types are various, each device has its own operation period, and if the light storage capacity is too large, the investment will be too large and resources will be wasted; otherwise, the power supply capacity is difficult to guarantee and still needs to rely on the grid to a large extent. In addition, the heating equipment has the largest power ratio in all loads and is mostly used at night, which is exactly opposite to the photovoltaic output characteristics. Configuring the energy storage system to transfer energy in time is a good idea to solve this problem. However, the selection of the energy storage capacity size also faces the problems of investment recovery period and energy storage capacity utilization rate.

[0004] The existing greenhouse light storage configuration method is mostly based on the optimal capacity and optimal access position of the energy storage configuration of the existing photovoltaic equipment of the sunlight greenhouse. The optimization algorithm for solving the optimization configuration model of the light storage capacity is currently mainly various intelligent search iteration algorithms, such as the improved algorithm of the neural network of the back propagation neural network (BPNN) and the intelligent algorithm of the particle swarm algorithm. The error of the method of a single learning machine and the typical particle swarm algorithm is relatively large, prone to overfitting, and the calculation process is complex. The typical particle swarm algorithm has problems such as slow convergence speed in the later period, premature convergence and falling into a local optimal solution. SUMMARY

[0005] The purpose of the present application is to provide a greenhouse power supply system light storage capacity configuration method, device, equipment and medium, which solves the problems of slow speed, low reliability and lack of light storage capacity optimization configuration based on the power supply and use mode, load characteristics and the like of the greenhouse of the solving method used by the existing greenhouse light storage configuration method.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a greenhouse power supply system light storage capacity configuration method, comprising:

[0008] Based on the typical daily load distribution data, a photovoltaic capacity configuration model and an energy storage capacity configuration model are established;

[0009] The photovoltaic capacity configuration model is solved by the MPPT algorithm, and the energy storage capacity configuration model is solved by the improved particle swarm algorithm to obtain an optimal solution.

[0010] According to the optimal solution, the photovoltaic and energy storage capacity of the greenhouse power supply system is configured.

[0011] Further, the typical daily load distribution data is obtained by collecting the data of the sunlight greenhouse power supply system and preprocessing the data.

[0012] The data of the sunlight greenhouse power supply system is collected, specifically, the power supply mode and load characteristic data related to the sunlight greenhouse power supply system are collected, including sunlight greenhouse illumination data, greenhouse structure characteristic data, equipment power consumption data, and crop power demand data.

[0013] Further, the preprocessing of the data specifically includes:

[0014] Based on the collected sunlight greenhouse illumination data and greenhouse structure characteristic data, a light estimation data set considering the factors of shading in the greenhouse, glass light transmittance, and solar position is established.

[0015] Based on the equipment power consumption data and crop power demand data, a load characteristic data set including the power demand at each time point of a typical day is established.

[0016] Based on the light estimation data set and the load characteristic data set, the typical daily load distribution data is generated by merging.

[0017] Further, the establishment of the photovoltaic capacity configuration model and the energy storage capacity configuration model based on the typical daily load distribution data specifically includes:

[0018] The photovoltaic capacity configuration model is:

[0019]

[0020] In the formula, is the installed capacity of the photovoltaic panel; is the greenhouse load simultaneity rate; is the power of each type of load in the greenhouse; is the number of the i-th load; is the number of load types; is the number of load types;

[0021] The objective function of the energy storage capacity configuration model is:

[0022]

[0023] In the formula, is the system construction cost; is the energy storage operation and maintenance cost; and To offset the cost of purchasing electricity; To reduce internet access revenue;

[0024] System construction cost The calculation formula is:

[0025]

[0026] In the formula: For the first Charging and discharging power within a time period; It is the rated capacity of energy storage; The unit cycle cost of the battery; This represents the maximum state of charge of the battery. This represents the minimum state of charge of the battery. Cost per unit capacity of energy storage; For energy storage life; For a calculation period;

[0027] The formula for calculating energy storage maintenance costs is:

[0028]

[0029] In the formula, This is a coefficient representing the proportion of energy storage maintenance costs.

[0030] The cost of electricity purchase is offset as follows:

[0031]

[0032] In the formula, for Power consumption at all times; for The output power of the photovoltaic module at any given time; for Energy storage and discharge power at all times; To offset the cost of purchasing electricity; The electricity price during that period; This represents the maximum output power of the energy storage.

[0033] The reduced internet access benefits are:

[0034]

[0035] In the formula, To reduce revenue from internet access; The on-grid electricity price for that period; The energy storage charging power during this period; This represents the maximum charging power for energy storage.

[0036] Furthermore, energy storage systems Moment is represented as:

[0037]

[0038] in the formula, is an energy storage system state of charge at the moment; is represented the calculation time interval;

[0039] The energy storage should satisfy the following constraint condition when charging and discharging:

[0040]

[0041] in the formula, is the current state of charge of the energy storage system; is the initial state of charge of the energy storage system; is the final state of charge of the energy storage system; is the minimum state of charge of the current energy storage system; is the maximum state of charge of the current energy storage system; is the current charging and discharging power of the energy storage system; is the maximum charging and discharging power of the energy storage system; is the minimum charging and discharging power of the energy storage system;

[0042] The power balance constraint is established:

[0043]

[0044] in the formula, is the grid input power.

[0045] Further, the solving of the photovoltaic capacity configuration model by the MPPT algorithm specifically includes:

[0046] The current-voltage equation of the photovoltaic cell is determined as:

[0047]

[0048] in the formula, is the current of the photovoltaic cell; is the first mathematical parameter; is the second mathematical parameter; is the voltage of the photovoltaic cell; is the short-circuit current under standard conditions; is the open-circuit voltage under standard conditions; is the maximum power point current under standard conditions; is the maximum power point voltage under standard conditions;

[0049] When solar irradiance and battery temperature are under non-standard conditions:

[0050]

[0051] In the formula: The temperature of the solar cell array under any radiation or temperature. Ambient temperature; K is the temperature coefficient of the solar cell; G is the irradiance.

[0052] Under actual irradiance and temperature conditions:

[0053]

[0054] In the formula, This is the reference illumination intensity under standard conditions; This is the reference temperature under standard conditions. This is the difference between the actual temperature and the reference temperature. Radiation intensity; This represents the relative difference in irradiance. This refers to the short-circuit current of the photovoltaic cell under actual conditions. This refers to the open-circuit voltage of the photovoltaic cell under actual conditions. This represents the maximum power point current of the photovoltaic cell under actual conditions. These are the maximum power point voltages of the photovoltaic cells under actual conditions; α is the first coefficient, α=0.0025, and β is the second coefficient;

[0055] By establishing the current-voltage equation of the photovoltaic cell, fitting the voltage and current of the photovoltaic cell based on the MPPT algorithm, obtaining the daily power output curve of the photovoltaic cell, and substituting it into the power balance constraint in the photovoltaic capacity configuration model, the photovoltaic capacity configuration result is calculated.

[0056] Furthermore, the solution to the energy storage capacity configuration model using the improved particle swarm optimization algorithm specifically involves:

[0057] Input the load distribution data of the solar greenhouse into the energy storage capacity configuration model and initialize parameters such as energy storage capacity;

[0058] The particle swarm optimization algorithm is improved by changing the inertia weight and increasing the compression factor:

[0059] Update the inertia weights of the particle swarm optimization algorithm. The value decreases linearly with increasing iteration number, and its expression is:

[0060]

[0061] In the formula, The maximum inertia weight; The minimum inertia weight; This represents the current iteration number; This represents the maximum number of iterations.

[0062] Calculate the individual fitness of each particle. For each random particle, set its fitness value Compared with the individual extreme value, if the fitness value If the value is greater than the individual extreme value, then the fitness value is used. Replace individual extreme values; simultaneously, for each particle, use its fitness value. Compared with the global extremum, if If the value is greater than its global extremum, then the individual fitness value is used to replace the global extremum;

[0063] Calculation algorithm speed Update energy storage capacity Objective function:

[0064]

[0065]

[0066] In the formula, For the updated algorithm speed; This represents the original speed of the algorithm. For energy storage capacity, For the updated energy storage capacity; The optimal solution for the population; The optimal solution for the community; Inertial weight; For individual learning factors; For population learning factors; and Each is a random number;

[0067] By processing the boundary conditions of the improved particle swarm optimization algorithm, the algorithm is iterated to obtain the optimal energy storage capacity configuration result of the energy storage capacity configuration model, and thus the optimal solution is obtained.

[0068] Secondly, the present invention provides a photovoltaic storage capacity configuration device for a greenhouse power supply system, comprising:

[0069] The model building module is used to build photovoltaic capacity configuration models and energy storage capacity configuration models based on typical daily load distribution data.

[0070] The solution module is used to solve the photovoltaic capacity configuration model using the MPPT algorithm and the energy storage capacity configuration model using the improved particle swarm optimization algorithm to obtain the optimal solution.

[0071] The capacity configuration module is used to configure the photovoltaic and energy storage capacity of the greenhouse power supply system according to the optimal solution.

[0072] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement a method for configuring the photovoltaic storage capacity of a greenhouse power supply system.

[0073] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements a method for configuring the photovoltaic storage capacity of a greenhouse power supply system as described in any one of the above.

[0074] The beneficial effects of this invention are as follows:

[0075] 1. This invention establishes photovoltaic (PV) capacity configuration models and energy storage capacity configuration models based on typical daily load distribution data. The PV capacity configuration model is solved using the MPPT algorithm, and the energy storage capacity configuration model is solved using an improved particle swarm optimization (PSO) algorithm to obtain the optimal solution. Based on the optimal solution, the PV and energy storage capacities of the greenhouse power supply system are configured. The optimal PV and energy storage capacity configuration results are obtained by solving the MPPT algorithm and the improved PSO algorithm. This method is fast, reliable, and overcomes the problems of slow convergence speed, premature convergence, and getting trapped in local optima inherent in existing algorithms.

[0076] 2. This invention converts the energy storage construction cost to a daily unit, comprehensively considers system construction cost, operation and maintenance cost, reduction of grid electricity purchase revenue and reduction of surplus electricity grid connection loss, takes minimizing the difference between daily cost and revenue as the optimization objective, uses energy storage state of charge and other constraints as constraints, and establishes an energy storage capacity configuration model in conjunction with energy balance constraints. By iteratively solving the problem using an improved particle swarm optimization algorithm, the optimal solution for the solar greenhouse power supply system can be obtained, making the method have the advantages of fast calculation speed and high reliability. Attached Figure Description

[0077] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0078] Figure 1 This is a schematic diagram of the greenhouse light and storage capacity configuration method according to an embodiment of the present invention;

[0079] Figure 2 This is the control process using the incremental conductivity method in an embodiment of the present invention;

[0080] Figure 3 This is a flowchart illustrating the particle fitness calculation process in an embodiment of the present invention.

[0081] Figure 4This is a structural block diagram of the greenhouse light and energy storage capacity configuration device according to an embodiment of the present invention;

[0082] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0083] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0084] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0085] Example 1

[0086] like Figure 1 As shown, a method for configuring the photovoltaic and energy storage capacity of a greenhouse power supply system includes:

[0087] S1: Based on the local natural environment characteristics and load demand of the solar greenhouse power supply system's photovoltaic storage capacity configuration, summarize the specific energy-consuming equipment configuration in a single solar greenhouse.

[0088] Specifically, the energy-consuming equipment in this plan may include:

[0089] Roller shutters, integrated water and fertilizer machines, water pumps, supplemental lighting, sprinkler systems, ventilation systems, electric refrigeration equipment, electric heating equipment, etc.

[0090] S2: Collect data from the power supply system of the solar greenhouse, preprocess the data from the power supply system of the solar greenhouse, and obtain typical daily load distribution data;

[0091] Step S2 includes the following:

[0092] S21: Obtain data on the power supply system of the solar greenhouse.

[0093] Specifically, the power supply system data for solar greenhouses includes power supply mode and load characteristic data;

[0094] Specifically, this includes data on the greenhouse's light intensity, structural characteristics, equipment power consumption, and crop electricity requirements. This data includes daily records covering different seasons and weather conditions.

[0095] The power supply mode data includes: the light data of the solar greenhouse and the characteristic data of the greenhouse structure; the power supply mode data specifically includes the greenhouse's light data and other renewable resource endowment, the greenhouse's distributed energy installation status, and the greenhouse's AC / DC power supply structure, etc.

[0096] Load characteristic data includes: power consumption data of equipment and power demand data of crops; specifically, load characteristic data is the energy consumption data of greenhouse equipment under different seasons and weather conditions in spring, summer, autumn and winter.

[0097] Greenhouse structural characteristic data include: greenhouse glass transmittance, sun position, greenhouse shading conditions, and other heat transfer coefficient parameters (when performing greenhouse heating calculations, the above-described greenhouse heat transfer parameters are described using thermal resistance), greenhouse heating methods, etc.

[0098] S22: Based on the collected light data and structural characteristic data of the solar greenhouse, establish a light estimation dataset that considers factors such as shading, glass transmittance, and sun position within the greenhouse.

[0099] The light estimation dataset is a simulated dataset of solar radiation received by the greenhouse, which is built based on power supply mode data.

[0100] S23: Based on the power consumption data of the equipment and the power demand data of the crops, establish a load characteristic dataset that includes the power demand at various time points of a typical day, reflecting the power consumption of different equipment and crops at different times.

[0101] The load characteristic dataset reflects the 24-hour electricity load distribution curves of a typical greenhouse day under different seasons and weather conditions, and is established based on the load characteristic data.

[0102] S24: Based on the light estimation dataset and the load characteristic dataset, typical daily load distribution data is generated by merging them to cover the electricity demand of solar greenhouses on typical days throughout the year.

[0103] It is understandable that there are several influencing factors in the configuration of the photovoltaic storage capacity of the power supply system of a solar greenhouse, such as the solar greenhouse's illumination data, the greenhouse structure's characteristic data, the equipment's power consumption data, and the crop's power demand data. In the above steps, the power supply and consumption mode and load characteristics involved in the configuration of the photovoltaic storage capacity of the solar greenhouse power supply system are transformed into typical daily load distribution data suitable for the photovoltaic storage capacity configuration model.

[0104] Typical daily load distribution data includes: greenhouse load simultaneity rate, power of various loads in the greenhouse, number of loads, number of load types, battery state of charge, charging and discharging power, rated energy storage capacity, energy storage charging and discharging power, output power of photovoltaic modules, and load power consumption, etc.

[0105] S3: Based on typical daily load distribution data, establish photovoltaic capacity configuration models and energy storage capacity configuration models;

[0106] S31: Distributed photovoltaic (PV) configuration should be based on greenhouse load. A smaller PV capacity configuration will increase grid power purchase costs; conversely, a larger PV capacity configuration will increase investment costs and reduce economic benefits. Because greenhouse equipment operates independently, and its operating hours and durations are difficult to determine, PV capacity configuration cannot be based solely on the total greenhouse equipment load. Considering these issues, a PV capacity configuration model that takes into account the greenhouse load simultaneity rate is established. The objective function of the PV capacity configuration model is:

[0107]

[0108] In the formula, For photovoltaic panel installed capacity; The greenhouse load simultaneity rate is the probability that all greenhouse equipment will operate at the same time. For the power of various loads inside the greenhouse; For the first Number of species; This represents the number of load types.

[0109] S32: Establish the optimization objective of the energy storage capacity configuration model: Using the minimum difference between intraday cost and benefit as the optimization objective, and comprehensively considering various costs and benefits of energy storage, the objective function of the energy storage capacity configuration model is:

[0110]

[0111] In the formula, For system construction costs; For energy storage operation and maintenance costs; To offset the cost of purchasing electricity; To reduce internet access revenue.

[0112] This energy storage capacity configuration model takes into account the cost of energy storage charging and discharging, that is, it converts the investment in the energy storage construction phase into the cost generated by a daily cycle of charging and discharging, and the system construction cost. The calculation formula is:

[0113]

[0114] In the formula: For the first Charging and discharging power within a time period; It is the rated capacity of energy storage; The unit cycle cost of the battery; This represents the maximum state of charge of the battery. This represents the minimum state of charge of the battery. Cost per unit capacity of energy storage; For energy storage life; This is a calculation period.

[0115] Energy storage maintenance costs include routine operation and maintenance, parts replacement, and labor costs. These costs are calculated by multiplying the construction cost by a proportional coefficient. The formula is as follows:

[0116]

[0117] In the formula, This is the ratio of energy storage maintenance costs.

[0118] Due to the heating needs of greenhouses in winter, the load is higher at night, showing an overall trend of lower load during the day and higher load at night. The photovoltaic (PV) output power distribution is the opposite: during the day, when sunlight is abundant, the theoretical output power is higher, while at night, almost no electricity is generated. Therefore, when an energy storage system is installed, the amount of PV power fed into the grid decreases during the day, resulting in lower grid connection revenue; at night, when discharging, it reduces energy consumption to the grid, offsetting some of the electricity purchase cost. This offsetting of electricity purchase cost can be expressed as:

[0119]

[0120] In the formula, for Power consumption at all times; for The output power of the photovoltaic module at any given time; for Energy storage and discharge power at all times; To offset the cost of purchasing electricity; The electricity price during that period; This represents the maximum output power of the energy storage.

[0121] The reduced internet access benefits can be expressed as:

[0122]

[0123] In the formula, To reduce revenue from internet access; The on-grid electricity price for that period; The energy storage charging power during this period; This represents the maximum charging power for energy storage.

[0124] S33: Establish the constraints for the photovoltaic capacity configuration model and energy storage capacity configuration model of the power supply system for solar greenhouses;

[0125] The energy storage unit in the system should maintain system energy balance. When the local load power is not equal to the output power of the photovoltaic modules, the energy storage system needs to release or absorb energy to the grid through the converter to balance the load energy demand. To characterize the remaining capacity of the energy storage system, the state of charge (SOC) of the energy storage system is introduced. Moment Represented as:

[0126]

[0127] In the formula, express The calculation time interval.

[0128] The following constraint must be met when energy storage is charged and discharged:

[0129]

[0130] In the formula, This represents the current state of charge of the energy storage system. This represents the initial state of charge of the energy storage system. This represents the final state of charge of the energy storage system. This represents the current minimum state of charge of the energy storage system. This represents the current maximum state of charge of the energy storage system. The charging and discharging power of the current energy storage system; This represents the maximum charging and discharging power of the energy storage system. This represents the minimum charging and discharging power of the energy storage system.

[0131] Establish power balance constraints:

[0132]

[0133] In the formula, It represents the power input to the power grid. It is positive when the transmission direction is from the power grid to the user side, and negative otherwise.

[0134] S4: Solve the photovoltaic capacity configuration model using the MPPT algorithm, and solve the energy storage capacity configuration model using the improved particle swarm optimization algorithm to obtain the optimal solution, i.e., the optimal photovoltaic-storage capacity.

[0135] S41: Solve the photovoltaic capacity configuration model using the MPPT algorithm;

[0136] Based on the photovoltaic effect, solar photovoltaic panels are simplified as constant current sources. A simple model, easily applicable in engineering, can be derived based on the current and voltage equations of photovoltaic cells. This model only requires four key parameters provided by the cell manufacturer, namely... , , , This allows us to obtain a simple engineering model of the array characteristics, namely the current-voltage equation of the photovoltaic cell. The current-voltage equation of the photovoltaic cell simplifies to:

[0137]

[0138] In the formula, The current of the photovoltaic cell; The first mathematical parameter; It is the second mathematical parameter; The voltage of the photovoltaic cell; This refers to the short-circuit current under standard conditions. This is the open-circuit voltage under standard conditions; This is the maximum power point current under standard conditions; This is the maximum power point voltage under standard conditions.

[0139] When solar irradiance and battery temperature are under non-standard conditions, the influence of ambient temperature must be considered. After collecting a large amount of data, the following formula is accurate enough for engineering applications.

[0140]

[0141] In the formula: The temperature of the solar cell array under any radiation or temperature. Ambient temperature; K is the temperature coefficient of the solar cell; G is the irradiance.

[0142] Under actual irradiance and temperature conditions, a new parametric equation was derived:

[0143]

[0144] In the formula, This is the reference illumination intensity under standard conditions; This is the reference temperature under standard conditions. This is the difference between the actual temperature and the reference temperature. Radiation intensity; This represents the relative difference in irradiance. This refers to the short-circuit current of the photovoltaic cell under actual conditions. This refers to the open-circuit voltage of the photovoltaic cell under actual conditions. This represents the maximum power point current of the photovoltaic cell under actual conditions. α represents the maximum power point voltage of the photovoltaic cell under actual conditions; α is the first coefficient, α=0.0025, β is the second coefficient, β=0.5, and c is the third coefficient, c=0.00288.

[0145] By establishing the current-voltage equation for photovoltaic (PV) cells, the voltage and current of PV cells are fitted using the incremental conductance method (MPPT algorithm), thereby obtaining the daily power output curve of the PV cells. This curve is then input into the power balance constraints of the PV capacity configuration model to calculate the PV capacity configuration result. Specifically:

[0146] Based on the current-voltage equation of photovoltaic cells, the MPPT algorithm is used to plot the theoretical maximum output power of photovoltaic cells over a day as a function of time. The incremental conductance method maximizes output power by changing the rate of change of the output current and voltage of the cell array. Let the output power of the cell array... Where U and I are the output voltage and output current, respectively, differentiating both sides of the equation with respect to voltage U, we get:

[0147]

[0148] At this point, there are three possible scenarios:

[0149] when When the value is greater than 0, the operating point of the photovoltaic module is to the left of the maximum power point, and it is necessary to add perturbation to increase the output voltage;

[0150] when When the value equals 0, the photovoltaic module output power is at its maximum, operating at the maximum power point, and disturbances should be stopped.

[0151] when When the value is less than 0, the photovoltaic module's operating point is to the right of the maximum power point. Therefore, disturbances should be reduced and control signals should be output.

[0152] Control process such as Figure 2 As shown;

[0153] Input the load distribution data of the solar greenhouse into the photovoltaic capacity configuration model and initialize parameters such as photovoltaic capacity;

[0154] The optimal photovoltaic capacity configuration result is obtained by solving the photovoltaic capacity configuration model using the MPPT algorithm.

[0155] S42: To ensure that the global optimal solution is found in the solution of the energy storage capacity configuration model and to improve the convergence speed in the later stage, the improved particle swarm optimization algorithm is used to solve the energy storage capacity configuration model to obtain the optimal solution.

[0156] The classic particle swarm optimization algorithm has problems such as slow convergence speed in the later stages, premature convergence, and getting trapped in local optima. This invention improves the particle swarm optimization algorithm by changing the inertia weight and increasing the compression factor.

[0157] Input the load distribution data of the solar greenhouse into the energy storage capacity configuration model and initialize parameters such as energy storage capacity;

[0158] S43: Improve the particle swarm optimization algorithm by changing the inertia weight and increasing the compression factor;

[0159] Update the inertia weights of the particle swarm optimization algorithm; the linear inertia weight method is the inertia weight method. The value decreases linearly with increasing iteration number, and its expression is:

[0160]

[0161] In the formula, The maximum inertia weight; The minimum inertia weight; This represents the current iteration number; This represents the maximum number of iterations.

[0162] Calculate the individual fitness of each particle. The solution steps are as follows: Figure 3 As shown, for each random particle, its fitness value is... Compared with the individual extreme value, if the fitness value If the value is greater than the individual extreme value, then the fitness value is used. Replace individual extreme values; simultaneously, for each particle, use its fitness value. Compared with the global extremum, if If the value is greater than its global extremum, then the individual fitness value is used to replace the global extremum;

[0163] Calculation algorithm speed Update energy storage capacity Objective function:

[0164]

[0165]

[0166] In the formula, For the updated algorithm speed; This represents the original speed of the algorithm. For energy storage capacity, For the updated energy storage capacity; The optimal solution for the population; The optimal solution for the community; Inertial weight; For individual learning factors; For population learning factors; and Each of the two numbers is a random number.

[0167] S44: Process the boundary conditions of the improved particle swarm optimization algorithm, perform algorithm iteration until the optimal solution is obtained, and use the optimal solution as the optimal energy storage capacity configuration result of the energy storage capacity configuration model to obtain the optimal solution.

[0168] S5: Configure the photovoltaic storage capacity of the greenhouse power supply system according to the optimal solution.

[0169] Example 2

[0170] like Figure 4 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a greenhouse power supply system photovoltaic storage capacity configuration device, comprising:

[0171] The model building module is used to build photovoltaic capacity configuration models and energy storage capacity configuration models based on typical daily load distribution data.

[0172] The solution module is used to solve the photovoltaic capacity configuration model using the MPPT algorithm and the energy storage capacity configuration model using the improved particle swarm optimization algorithm to obtain the optimal solution.

[0173] The capacity configuration module is used to configure the photovoltaic and energy storage capacity of the greenhouse power supply system according to the optimal solution.

[0174] Example 3

[0175] like Figure 5 As shown, the present invention also provides an electronic device 100 for implementing a method for configuring the photovoltaic storage capacity of a greenhouse power supply system;

[0176] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0177] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the photovoltaic storage capacity configuration method of a greenhouse power supply system in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0178] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0179] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0180] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for configuring the photovoltaic storage capacity of a greenhouse power supply system, and the processor 102 can execute multiple instructions to achieve the following:

[0181] Based on typical daily load distribution data, photovoltaic capacity configuration models and energy storage capacity configuration models are established.

[0182] The photovoltaic capacity configuration model is solved by the MPPT algorithm, and the energy storage capacity configuration model is solved by the improved particle swarm optimization algorithm to obtain the optimal solution.

[0183] Based on the optimal solution, the photovoltaic and energy storage capacity of the greenhouse power supply system is configured.

[0184] Example 4

[0185] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0186] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0187] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0188] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0190] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for configuring the light storage capacity of a greenhouse power supply system, characterized in that, The application relates to a greenhouse power supply system photovoltaic and energy storage capacity configuration method. The photovoltaic capacity configuration model is solved through an MPPT algorithm, and the energy storage capacity configuration model is solved through an improved particle swarm algorithm to obtain an optimal solution. The optimal solution is used to configure photovoltaic and energy storage capacities of the greenhouse power supply system. The photovoltaic capacity configuration model is solved through an MPPT algorithm, and the energy storage capacity configuration model is solved through an improved particle swarm algorithm to obtain an optimal solution. The photovoltaic capacity configuration model is solved through an MPPT algorithm, and the energy storage capacity configuration model is solved through an improved particle swarm algorithm to obtain an optimal solution. The energy storage maintenance cost calculation formula is: wherein is the installed capacity of the photovoltaic panel; is the simultaneous rate of the greenhouse loads; is the power of each type of load in the greenhouse; is the number of the th type of load; is the number of load types; The offset power purchase cost is: In the formula, is the system construction cost; is the energy storage operation and maintenance cost; is the offset electricity purchase cost; is the reduced on-grid income; System construction cost The calculation formula is: In the formula: is the first is the charging and discharging power in the time period; is the energy storage rated capacity; is the battery unit cycle cost; is the maximum battery state of charge; is the minimum battery state of charge; is the energy storage unit capacity cost; is the energy storage life; is a calculation period; The reduced grid access income is: In the formula, is the energy storage maintenance cost proportionality coefficient; The typical daily load distribution data is obtained by collecting greenhouse power supply system data and preprocessing the data. wherein, is the load consumption power at the moment; is the output power of the photovoltaic module at the moment; is the energy storage discharge power at the moment; is the cost of electricity purchase; is the grid electricity price in the period; is the maximum output power of the energy storage; The greenhouse power supply system data is collected, and the data preprocessing is specifically as follows. In the formula, is the reduction of the online income; is the online electricity price in the period; is the energy storage charging power in the period; is the maximum energy storage charging power.

2. The method of claim 1, wherein, Based on the collected sunlight greenhouse light data and greenhouse structure characteristic data, a light estimation data set considering the factors of greenhouse shading, glass light transmittance and solar position is established. Based on the equipment power consumption data and crop power demand data, a load characteristic data set including power demand at each time point of a typical day is established.

3. The method of claim 2, wherein the light storage capacity of the greenhouse power supply system is configured by, Based on the light estimation data set and the load characteristic data set, the typical daily load distribution data is generated.

4. The greenhouse power supply system photovoltaic and energy storage capacity configuration method according to claim 1, characterized in that: The energy storage should satisfy the following constraint condition when charging and discharging: The power balance constraint is established. The photovoltaic capacity configuration model is solved through an MPPT algorithm, and the energy storage capacity configuration model is solved through an improved particle swarm algorithm to obtain an optimal solution. Energy storage system Momentary is expressed as: In the formula, a power storage system state of charge at the time instant; denotes the calculation time interval; The current-voltage equation of the photovoltaic cell is determined as: wherein SoC is the current energy storage system state of charge; SoC0 is the initial energy storage system state of charge; SoCf is the final energy storage system state of charge; SoCmin is the current energy storage system minimum state of charge; SoCmax is the current energy storage system maximum state of charge; P is the current energy storage system charge and discharge power; Pmax is the maximum energy storage system charge and discharge power; Pmin is the minimum energy storage system charge and discharge power; When the solar radiation intensity and the cell temperature are in a non-standard condition: In the formula, is the grid input power.

5. The method of claim 4, wherein, In the actual irradiance and temperature condition: The photovoltaic cell daily output curve is obtained by establishing the current-voltage equation of the photovoltaic cell, fitting the photovoltaic cell voltage and current based on the MPPT algorithm, and bringing the photovoltaic cell daily output curve into the power balance constraint in the photovoltaic capacity configuration model. wherein is the current of the photovoltaic cell; is the first mathematical parameter; is the second mathematical parameter; is the voltage of the photovoltaic cell; is the short circuit current under standard conditions; is the open circuit voltage under standard conditions; is the maximum power point current under standard conditions; is the maximum power point voltage under standard conditions; The photovoltaic capacity configuration model is solved through an MPPT algorithm, and the energy storage capacity configuration model is solved through an improved particle swarm algorithm to obtain an optimal solution. wherein: T is the temperature of the solar cell array at any radiation or temperature; ambient temperature; K is the solar cell temperature coefficient; G is the irradiance intensity; The load distribution related data of the sunlight greenhouse is input into the energy storage capacity configuration model to initialize the energy storage capacity and other parameters. wherein is the reference light intensity at standard conditions; is the reference temperature at standard conditions; is the difference between the actual temperature and the reference temperature; is the irradiance; is the relative irradiance difference; is the short-circuit current of the photovoltaic cell under actual conditions; is the open-circuit voltage of the photovoltaic cell under actual conditions; is the maximum power point current of the photovoltaic cell under actual conditions; is the maximum power point voltage of the photovoltaic cell under actual conditions; α is a first coefficient, α = 0.0025, and β is a second coefficient; The particle swarm algorithm is improved by changing the inertia weight and adding a compression factor.

6. The method of claim 5, wherein, The boundary conditions of the improved particle swarm algorithm are processed, the algorithm is iterated, the optimal energy storage capacity configuration result of the energy storage capacity configuration model is obtained, and the optimal solution is obtained. The application relates to a greenhouse power supply system photovoltaic and energy storage capacity configuration method. The model establishment module is used for establishing the photovoltaic capacity configuration model and the energy storage capacity configuration model based on the typical daily load distribution data. updating the inertia weight of the particle swarm optimization algorithm, inertia weight linearly decreases with the number of iterations, and its expression is: wherein is the maximum inertia weight; is the minimum inertia weight; is the current iteration number; is the maximum iteration number; Calculate the individual fitness of each particle. For each random particle, set its fitness value. Compared with the individual extreme value, if the fitness value If the value is greater than the individual extreme value, then the fitness value is used. Replace individual extreme values; simultaneously, for each particle, use its fitness value. Compared with the global extremum, if If the value is greater than its global extremum, then the individual fitness value is used to replace the global extremum; Computing algorithm speed , updating energy storage capacity Objective functions: wherein, is the updated algorithm speed; is the original algorithm speed; is the energy storage capacity, is the updated energy storage capacity; is the population optimal solution; is the colony optimal solution; is the inertia weight; is the individual learning factor; is the population learning factor; and are two random numbers, respectively. The solving module is used for solving the photovoltaic capacity configuration model through an MPPT algorithm, solving the energy storage capacity configuration model through an improved particle swarm algorithm, and obtaining an optimal solution.

7. A greenhouse power supply system light storage capacity configuration device for implementing the greenhouse power supply system light storage capacity configuration method of claim 1, characterized by, The capacity configuration module is used for configuring the photovoltaic and energy storage capacities of the greenhouse power supply system according to the optimal solution. ​ ​ ​ 8. An electronic device, comprising: The application relates to a computer readable storage medium storing a computer program for implementing a method for configuring the light storage capacity of a greenhouse power supply system according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one instruction which is executed by the processor to implement a method for configuring the light storage capacity of a greenhouse power supply system according to any one of claims 1 to 6.

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