A power supply method of photovoltaic energy storage considering industrial production load and time-of-use electricity price

Abstract

The application discloses a kind of photovoltaic storage power supply and intelligent scheduling method considering industrial production load and time-of-use electricity price comprehensively, comprising the following steps: first, with the minimum cost as the goal to establish the calculation model of photovoltaic panel operating area, the model is with radiation intensity, temperature and other factors affecting photovoltaic power generation as constraint condition, while meeting the factory load energy consumption, realize the lowest economic cost;Then, with the minimum cost as the goal to establish the model of energy storage cabinet operating capacity calculation, the model considers photovoltaic power generation constraint, factory load power consumption constraint and energy storage cabinet cost constraint, realize the lowest cost while meeting the factory energy storage requirements;Finally, establish factory scheduling model, combine photovoltaic panel modeling and energy storage modeling and factory energy consumption, give scheduling strategy and calculate the optimal value of photovoltaic panel operating area and energy storage cabinet operating capacity.The method proposed in the application takes the minimum operating cost as the goal, calculates the optimal photovoltaic panel operating area, energy storage cabinet operating capacity, controls industrial site work, can effectively reduce factory energy consumption expenditure, while improving site operation efficiency.

Description

Technical Field

[0001] This invention belongs to the field of smart grids, specifically relating to a photovoltaic-storage power supply and intelligent dispatching method that takes into account industrial production load and time-of-use pricing. Background Technology

[0002] With the accelerating pace of industrialization, energy consumption in the industrial sector is increasing year by year, placing ever greater demands on the power system. Simultaneously, load and price fluctuations in the power system present new energy management challenges for industrial enterprises. Traditional power systems often struggle to adapt flexibly to rapid load changes in response to industrial production demands, leading to energy waste and power instability issues. Furthermore, fluctuating electricity prices present new challenges to enterprise energy management, making electricity costs difficult to predict and control. The utilization of renewable energy sources such as solar power offers a possibility for solving these problems. However, current technologies lack a method that can fully utilize these resources and combine them with energy storage technology to provide a stable and efficient power supply. Existing methods do not rationally allocate the power supply capacity of solar power generation and energy storage systems based on real-time changes in industrial production load. Moreover, they do not fully consider the impact of time-of-use pricing and the rational scheduling of on-site energy consumption. Therefore, there is an urgent need to design a photovoltaic-storage power supply and intelligent scheduling method that comprehensively considers industrial production load and time-of-use pricing. This is of great significance for optimizing energy management in industrial enterprises and improving energy utilization efficiency on-site. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings in existing factory energy consumption problems by providing a photovoltaic-storage power supply and intelligent dispatch method that considers industrial production load and time-of-use electricity pricing. First, based on factors such as factory load, the power generation and cost of solar photovoltaic panels, and the cost of energy storage cabinets, calculation models are established for the operating area of ​​photovoltaic panels and the operating capacity of energy storage cabinets. On this basis, taking into full account time-of-use electricity pricing and the actual energy consumption of the factory, a dispatch strategy is designed with the goal of minimizing energy costs. A model is established that comprehensively considers the photovoltaic-storage power supply system and time-of-use electricity pricing for intelligent dispatch strategies. The model ultimately provides suggested operating areas for photovoltaic panels and operating capacities for energy storage cabinets.

[0004] The technical solution adopted by the present invention to achieve the above objectives is as follows:

[0005] A photovoltaic-storage power supply and intelligent dispatch method that comprehensively considers industrial production load and time-of-use pricing includes:

[0006] S1. This model uses the factors affecting photovoltaic power generation as constraints and aims to minimize the construction cost to model the operating area of ​​photovoltaic panels.

[0007] S2. Combining the constraints of photovoltaic power generation, factory load power consumption, and energy storage cabinet construction cost, model the energy storage cabinet operating capacity with the goal of minimizing construction cost.

[0008] S3. Establish a factory power dispatching model, combine photovoltaic panel modeling and energy storage modeling with the factory's energy consumption, and provide intelligent dispatching strategies to output commands to control the factory's energy sources;

[0009] S4. Calculate the optimal values ​​for photovoltaic panel area and energy storage cabinet capacity with the goal of minimizing the total energy cost of the factory.

[0010] S5. Based on the optimal photovoltaic panel area and energy storage cabinet capacity, control the opening and closing of photovoltaic panels at the photovoltaic operation site and energy storage cabinets at the energy storage site to achieve reasonable scheduling.

[0011] The modeling of the photovoltaic panel operating area includes:

[0012] 1) Calculate the hourly power generation per square meter of photovoltaic panels based on historical sunshine data;

[0013] ppg hour =P p (t)×1m 2 ×1h;

[0014] Where P p (t) represents the photovoltaic output power;

[0015] 2) Analyze the factory's historical load data and available installation area to obtain the constraint range for the photovoltaic panel area;

[0016]

[0017] Among them, ppg hour This represents the power generation per square meter of photovoltaic panels per unit time; energy_consumption_day represents the daily power consumption of the factory; historical load data represents the daily power consumption and hourly power consumption of the factory.

[0018] 3) Model the area of ​​the photovoltaic panel to obtain the monthly cost of the photovoltaic panel:

[0019]

[0020] Where C_area is the cost per unit area of ​​photovoltaic panels; servicelife year This refers to the lifespan of the device.

[0021] The energy storage cabinet's operating capacity modeling includes:

[0022] 1) Determine the capacity range of energy storage cabinets based on the market's energy storage cabinet scheduling situation;

[0023]

[0024] Where pv_area is the area of ​​the photovoltaic panel; ppg hour This represents the power generation per square meter of photovoltaic panels per unit time; energy_consumption_day represents the daily power consumption of the factory load; the constraints are as follows:

[0025] a)P ESS Actual charge / discharge power of the energy storage system: -P max ≤P ESS ≤P max ;P max This refers to the rated power of the energy storage system.

[0026] b)E ESS Indicates the rated capacity of the energy storage cabinet: -E ESS ×C max ≤P ESS ≤E ESS ×C max C max This indicates the maximum discharge rate of the energy storage cabinet;

[0027] c) State of charge constraint: 5% ≤ SoC ≤ 95%;

[0028] d) represents the electrical charge E of the energy storage system at time t. ESS (t) Constraints: E ESS ×5%≤E ESS (t)≤E ESS ×95%;

[0029] e) Design constraints for photovoltaic panel area: Among them, E battery V represents the rated capacity of a single battery cell in the energy storage cabinet. battery This refers to the volume of a single battery cell in the energy storage cabinet.

[0030] 2) Model the capacity of the energy storage cabinet to obtain the monthly cost of the energy storage cabinet:

[0031]

[0032] Among them, C_E ESS For the unit cost of energy storage systems, servicelife year This section indicates the service life of the energy storage cabinet.

[0033] The time-of-use pricing intelligent dispatch strategy includes:

[0034] 1) Off-peak scheduling strategy:

[0035] Determine whether the remaining power in the energy storage cabinet is sufficient to meet the power demand during peak and off-peak hours;

[0036] If sufficient, the energy storage cabinet will supply power, and the power supply amount will be the remaining power minus the power consumption during peak and off-peak hours;

[0037] If the power supply is insufficient, the main grid will supply power to reserve the energy storage cabinet for priority use during peak and off-peak periods;

[0038] 2) Normal Segment Scheduling Strategy:

[0039] Determine whether the photovoltaic power generation can meet the electricity demand during normal periods;

[0040] If the conditions are met, photovoltaic power will be supplied, and excess electricity will be stored in an energy storage cabinet or traded with the main power grid.

[0041] If the requirements are not met, the energy storage cabinet will supply power, taking into account the discharge efficiency of the energy storage cabinet. If the power supply is insufficient, the main grid will supplement it.

[0042] 3) Peak-hour scheduling strategy:

[0043] During the period from 8:00 to 12:00, priority will be given to using solar power, and excess electricity will be stored or traded with the main power grid.

[0044] During the period from 17:00 to 21:00, priority will be given to power supply from photovoltaic and energy storage cabinets, and the main grid will supplement power if insufficient.

[0045] The total energy cost of the factory is the sum of the cost of the photovoltaic panels, the cost of the energy storage cabinet, and the electricity cost after the intelligent scheduling strategy; by finding the minimum value of the total cost, the operating area of ​​the photovoltaic panels and the operating capacity of the energy storage cabinet are output.

[0046] The total monthly energy cost of the factory is obtained by converting the above costs to monthly costs. The calculation formula is as follows:

[0047]

[0048]

[0049] Where: Cost is the total monthly cost converted to the monthly level;

[0050] C 转换器 This refers to the inverter cost calculated on a monthly basis.

[0051] n is the number of inverters;

[0052] C_converter is the cost of a single converter;

[0053] Time-of-use pricing for the main power grid;

[0054] energy_pg t The amount of electricity supplied from the main power grid to the load.

[0055] A photovoltaic-storage power supply and intelligent dispatching device that comprehensively considers industrial production load and time-of-use electricity pricing includes a front-end interface and a back-end interface. The back-end interface is equipped with a memory and a processor. The processor stores a program, and when the processor loads the program, it executes the method steps described above. With the goal of minimizing the total energy cost of the factory, it calculates the optimal values ​​of the photovoltaic panel operating area and the energy storage cabinet operating capacity, and outputs instructions to control the opening and closing of the photovoltaic panels at the photovoltaic operation site and the energy storage cabinet at the energy storage site to achieve reasonable dispatching. The front-end interface is used to collect user instructions, provide users with a visualized energy consumption scenario, and process and result data of calculation and control.

[0056] The advantages and beneficial effects of this invention are as follows:

[0057] 1. This invention optimizes the operating area of ​​photovoltaic panels and the operating capacity of energy storage cabinets by comprehensively considering industrial production load and time-of-use electricity prices, so that the system can maximize the use of solar power generation at different times, reduce dependence on the main power grid, and thus effectively reduce energy costs.

[0058] 2. This invention can flexibly schedule photovoltaic power generation, energy storage cabinet charging and discharging, and main grid power supply according to time-of-use electricity prices and actual factory load conditions, so that the system can achieve the optimal power supply scheme at each time period, improve energy utilization efficiency, reduce peak and valley differences in electricity consumption, and further improve the overall system efficiency.

[0059] 3. This invention optimizes the area of ​​photovoltaic panels and the operating capacity of energy storage cabinets, making it more suitable for actual factory conditions, reducing unnecessary excess equipment, and helping to reduce manufacturing and installation costs. Attached Figure Description

[0060] Figure 1 This is a flowchart of a photovoltaic-storage power supply and intelligent dispatching method that comprehensively considers industrial production load and time-of-use electricity pricing, as an embodiment of the present invention.

[0061] Figure 2 This is a flowchart illustrating the photovoltaic panel area calculation and modeling process in an embodiment of the present invention.

[0062] Figure 3 This is a flowchart illustrating the energy storage capacity calculation and modeling process in an embodiment of the present invention.

[0063] Figure 4 This is a flowchart of the scheduling method according to an embodiment of the present invention. Detailed Implementation

[0064] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0066] The technical solution adopted in this invention is a photovoltaic-storage power supply and intelligent dispatching method that considers industrial production load and time-of-use pricing, such as... Figure 1 As shown, it includes the following steps:

[0067] Step 1: Model the calculation of the photovoltaic panel operating area with the construction cost as the objective, such as... Figure 2 As shown, the specific process is as follows:

[0068] Step 1-1: Calculate the power generation per square meter of photovoltaic panels based on historical sunshine data, and then calculate the daily power generation per square meter of photovoltaic panels. The power generation of photovoltaic power is related to factors such as solar radiation intensity and ambient temperature. The calculation formula is as follows:

[0069]

[0070] T c =T e +30G AC / 1000

[0071] Power generation per unit area of ​​photovoltaic panels per hour: ppg hour =P p (t)×1m 2 ×1h

[0072] Among them, P p (t) represents the photovoltaic output power;

[0073] P STC For test conditions (G) STC ,T c Maximum test power under ( );

[0074] G AC Radiation intensity;

[0075] δ is the power temperature coefficient, typically taken as -0.47% K;

[0076] T c The operating temperature of the photovoltaic panel;

[0077] T r For reference temperature, 25 degrees Celsius is generally taken;

[0078] G STC The radiation intensity under standard test conditions is typically taken as 1000 W / m². 2 ;

[0079] ppg hour This refers to the power generation per square meter of photovoltaic panels per unit time (hour).

[0080] T e Ambient temperature;

[0081] Steps 1-2: Based on the factory's historical load data, calculate the factory's total daily electricity consumption and hourly load electricity consumption. The daily electricity consumption is the sum of the hourly load electricity consumption. Compare this to historical daily electricity consumption data to obtain the maximum historical daily electricity consumption. In this embodiment of the invention, the desired daily photovoltaic power generation can meet the maximum daily load electricity consumption; therefore, the constraint condition for the photovoltaic panel area is:

[0082]

[0083]

[0084] Where: energy_consumption_day is the daily electricity consumption of the factory load;

[0085] energy_consumption_hour is the hourly electricity consumption of the factory load;

[0086] pv_area represents the area of ​​the photovoltaic panel;

[0087] Steps 1-3: Considering the actual situation of the factory, if the maximum area of ​​the factory building used for installing photovoltaic panels is max_area, then the constraints that the photovoltaic panel area design should meet are:

[0088] pv_area≤max_area

[0089] Steps 1-4 convert the photovoltaic (PV) construction cost into a monthly cost, providing unified data support for intelligent scheduling. The cost conversion formula is as follows:

[0090]

[0091] C_area represents the cost of photovoltaic panels per unit area;

[0092] servicelifeyear The service life of the device;

[0093] Step 2: Model the energy storage operation capacity calculation with construction cost as the objective, such as... Figure 3 As shown, the specific process is as follows:

[0094] Step 2-1: When designing an energy storage cabinet, the charging and discharging power of the energy storage system must not exceed the rated power, which can be expressed as:

[0095] -P max ≤P ESS ≤P max

[0096] Among them, P ESS P represents the actual charge and discharge power of the energy storage system. max This is the rated power of the energy storage system.

[0097] Use C max E represents the maximum discharge rate of the energy storage cabinet. ESS The rated capacity of the energy storage cabinet is indicated by:

[0098] -E ESS ×C max ≤P ESS ≤E ESS ×C max

[0099] State of Charge (SOC) Constraint: The depth of charge / discharge (DoD) for industrial and commercial lithium iron phosphate batteries is set to 90%. The SOC window is [5%, 95%], meaning: 5% ≤ SOC ≤ 95%.

[0100] Furthermore, the power constraint requires the real-time power of the energy storage system to be within the upper and lower limits of the power limit. That is, when designing scheduling strategies to mobilize the energy storage cabinets in the future, this limitation should be taken into account.

[0101] E ESS ×5%≤E ESS (t)≤E ESS ×95%

[0102] Where E ESS (t) represents the amount of electricity in the energy storage system at time t.

[0103] Step 2-2: The design of the energy storage cabinet capacity is related to the photovoltaic power generation and the factory's load power consumption. To ensure that the energy storage cabinet can meet the factory's power dispatching needs without wasting the photovoltaic power generation, the initial total capacity of the energy storage cabinet is designed as the absolute value of the difference between the maximum daily photovoltaic power generation and the minimum daily load power consumption.

[0104]

[0105] Where pv_area is the area of ​​the photovoltaic panel; ppg hour This represents the power generation per square meter of photovoltaic panels per unit time; energy_consumption_day represents the daily power consumption of the factory load.

[0106] Steps 2-3: If the maximum space available for the factory to house the energy storage cabinet is max_space, then the constraints that the photovoltaic panel area design must meet are: Among them, E battery V represents the rated capacity of a single battery cell in the energy storage cabinet. battery This refers to the volume of a single battery cell in the energy storage cabinet.

[0107] Steps 2-4 convert the construction cost of the energy storage system to a monthly cost, providing unified data support for intelligent scheduling. The cost conversion formula is as follows:

[0108]

[0109] Where C_E ESS This refers to the unit cost of an energy storage system. year This section indicates the service life of the energy storage cabinet.

[0110] Step 3: Based on Steps 1 and 2, and taking into account industrial production load and time-of-use pricing, design a scheduling method. The scheduling method is time-segmented scheduling, with a time interval of 1 hour. After each hour's scheduling ends, six values ​​are returned: the amount of electricity directly supplied to the load by the photovoltaic system, the total amount of electricity charged into the energy storage cabinet each hour, the total amount of electricity released from the energy storage cabinet each hour, the amount of electricity directly supplied by the power grid each hour, the amount of electricity supplied by the power grid to the energy storage cabinet each hour, and the total amount of electricity remaining in the energy storage cabinet after the hour's scheduling ends.

[0111] like Figure 4 As shown, the specific scheduling process is as follows:

[0112] The off-peak scheduling method is as follows:

[0113] First, read the remaining electricity in the energy storage cabinet after the previous day's scheduling was completed;

[0114] If (remaining power in the energy storage cabinet - peak load power consumption) is greater than the normal load power consumption, the energy storage cabinet will supply power, and the power supply amount will be (remaining power in the energy storage cabinet - peak load power consumption - normal load power consumption); if the remaining power in the energy storage cabinet is low or the energy storage cabinet's discharge power is limited, it cannot fully meet the load power consumption during the off-peak period for a certain period of time, then the main power grid will supply power to the load.

[0115] If (remaining power in the energy storage cabinet - peak load power consumption) is less than the normal load power consumption, priority will be given to power supply from the main grid to ensure that the power in the energy storage cabinet is used during peak and normal periods.

[0116] The normal segment scheduling method is as follows:

[0117] If the photovoltaic power generation can meet the load demand during that period, then the load demand during that period will be entirely supplied by photovoltaic power, and the excess power will be stored in the energy storage cabinet. If the energy storage cabinet is full, the excess power will be traded with the main grid.

[0118] If the photovoltaic power generation is insufficient to meet the load demand during a certain period, then energy storage cabinets will be used for power supply, taking into account the photovoltaic power generation. At the same time, the discharge efficiency of the energy storage cabinets will be taken into account. If the load power supply is insufficient during a certain period due to the discharge efficiency of the energy storage cabinets, then the main grid power supply will be used as a supplement.

[0119] The peak-hour scheduling method is as follows:

[0120] During periods of high solar power generation, solar power is prioritized. If photovoltaic (PV) power generation is sufficient to fully support peak load demand, excess solar power is stored in energy storage cabinets. However, considering the charging power limitations of these cabinets, charging is conducted without damaging the storage batteries. If the storage cabinets are full, excess power is traded with the main grid. If PV power generation is insufficient to support peak load demand, energy storage cabinets are prioritized. If the energy storage cabinets' power supply is insufficient or discharge limitations prevent either PV or storage cabinet power from meeting load demand during a given period, then power from the main grid is considered.

[0121] Since solar power generation is insufficient to fully meet the load demand, energy storage cabinets are used for power supply while giving priority to photovoltaic power generation. If the power supply of the energy storage cabinet is insufficient or due to discharge limitations, neither photovoltaic nor energy storage cabinet power supply can meet the load demand at a certain time, then the main grid power supply will be considered.

[0122] Cost conversion:

[0123] The design of the photovoltaic panel operating area ensures that photovoltaic power generation can fully power the factory while avoiding energy waste and increased construction costs caused by excessive power generation; the design of the reasonable energy storage cabinet operating capacity provides a guarantee for energy storage; the photovoltaic-storage power supply system, combined with intelligent dispatching strategies, enables enterprises to achieve energy conservation and carbon reduction while minimizing electricity costs and maximizing economic benefits.

[0124] The formula for calculating the monthly electricity cost of the factory after adopting a photovoltaic-storage power supply system and a time-of-use scheduling strategy is as follows:

[0125] The monthly operating cost of photovoltaic panels is calculated by multiplying the cost per square meter of photovoltaic panels by the area of ​​the photovoltaic panels, then converting it to an annual cost based on the service life, and finally dividing by 12 to get the monthly cost. The monthly operating cost of energy storage cabinets is calculated by multiplying the cost of each energy storage battery by the number of energy storage batteries, then converting it to an annual cost based on the service life, and finally dividing by 12 to get the monthly cost. The monthly operating cost of converters is calculated by multiplying the cost of each converter by the number of converters, then converting it to an annual cost based on the service life, and finally dividing by 12 to get the monthly cost. The monthly cost of purchasing electricity from the main grid is calculated by multiplying the time-of-use electricity price for each day of the month by the hourly electricity purchase amount from the main grid and summing the results.

[0126] The calculation formula is as follows:

[0127]

[0128]

[0129]

[0130] Where: Cost is the total monthly cost converted to the monthly level;

[0131] C 光伏 This is to calculate the photovoltaic cost expenditure for the following month;

[0132] pv_area represents the area of ​​the photovoltaic panel;

[0133] C_area represents the cost of photovoltaic panels per unit area;

[0134] servicelife year The service life of the device;

[0135] C 储能柜 This is to calculate the cost of the energy storage cabinets for the month to follow;

[0136] C_E ESS This refers to the unit cost of the energy storage cabinet.

[0137] C 转换器 This refers to the converter manufacturing cost converted to the next month.

[0138] n is the number of converters;

[0139] C_converter is the cost of a single converter;

[0140] Time-of-use pricing for the main power grid;

[0141] energy_pg t The amount of electricity supplied from the main power grid to the load;

[0142] This invention also provides a photovoltaic-storage power supply and intelligent dispatching device that comprehensively considers industrial production load and time-of-use electricity pricing. It includes a front-end interface and a back-end interface. The back-end interface includes a memory and a processor. The processor stores a program, and when the processor loads the program, it executes the method steps described above. With the goal of minimizing the total energy cost of the factory, it calculates the optimal values ​​for the operating area of ​​the photovoltaic panels and the operating capacity of the energy storage cabinet, and outputs instructions to control the opening and closing of the photovoltaic panels at the photovoltaic operation site and the energy storage cabinet at the energy storage site, achieving reasonable dispatching. The front-end interface is used to collect user instructions, provide users with a visualized energy consumption scenario, and process and result data for calculation and control.

[0143] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for photovoltaic-storage power supply and intelligent dispatch that comprehensively considers industrial production load and time-of-use electricity pricing, characterized in that, include: S1. The model uses the factors affecting photovoltaic power generation as constraints and aims to minimize the construction cost to model the operating area of ​​photovoltaic panels. The modeling of the photovoltaic panel operating area includes: 1) Calculate the hourly power generation per square meter of photovoltaic panels based on historical sunshine data; ； in Photovoltaic output power; 2) Analyze the factory's historical load data and available installation area to obtain the constraint range for the photovoltaic panel area; ； in This represents the power generation per square meter of photovoltaic panels per unit time. This represents the factory's daily electricity consumption; historical load data includes both daily and hourly electricity consumption. 3) Model the area of ​​the photovoltaic panel to obtain the monthly cost of the photovoltaic panel: ； in, Cost per unit area of ​​photovoltaic panels; The service life of the device; S2. Combining the constraints of photovoltaic power generation, factory load power consumption, and energy storage cabinet construction cost, model the energy storage cabinet operating capacity with the goal of minimizing construction cost. S3. Establish a factory power dispatch model, combining photovoltaic panel modeling, energy storage modeling, and factory energy consumption data, to provide an intelligent dispatch strategy for outputting instructions to control the factory's energy sources; the time-of-use pricing-based intelligent dispatch strategy includes: 1) Off-peak scheduling strategy: Determine whether the remaining power in the energy storage cabinet is sufficient to meet the power demand during peak and off-peak hours; If sufficient, the energy storage cabinet will supply power, and the power supply amount will be the remaining power minus the power consumption during peak and off-peak hours; If the power supply is insufficient, the main grid will supply power to reserve the energy storage cabinet for priority use during peak and off-peak periods; 2) Normal Segment Scheduling Strategy: Determine whether the photovoltaic power generation can meet the electricity demand during normal periods; If the conditions are met, photovoltaic power will be supplied, and excess electricity will be stored in an energy storage cabinet or traded with the main power grid. If the requirements are not met, the energy storage cabinet will supply power, taking into account the discharge efficiency of the energy storage cabinet. If the power supply is insufficient, the main grid will supplement it. 3) Peak-hour scheduling strategy: During the period from 8:00 to 12:00, priority will be given to using solar power, and excess electricity will be stored or traded with the main power grid. From 17:00 to 21:00, priority will be given to power supply from photovoltaic and energy storage cabinets, with the main grid supplementing if insufficient. S4. Calculate the optimal values ​​for photovoltaic panel area and energy storage cabinet capacity with the goal of minimizing the total energy cost of the factory. S5. Based on the optimal photovoltaic panel area and energy storage cabinet capacity, control the opening and closing of photovoltaic panels at the photovoltaic operation site and energy storage cabinets at the energy storage site to achieve reasonable scheduling.

2. The photovoltaic-storage power supply and intelligent dispatching method that comprehensively considers industrial production load and time-of-use pricing as described in claim 1, characterized in that, The energy storage cabinet's operating capacity modeling includes: 1) Determine the capacity range of energy storage cabinets based on the market's energy storage cabinet scheduling situation; ； in, The area of ​​the photovoltaic panel; This represents the power generation per square meter of photovoltaic panels per unit time. This represents the daily electricity consumption of the factory. 2) Model the capacity of the energy storage cabinet to obtain the monthly cost of the energy storage cabinet: ； in, The unit cost of the energy storage system This section indicates the service life of the energy storage cabinet.

3. The photovoltaic-storage power supply and intelligent dispatching method that comprehensively considers industrial production load and time-of-use pricing as described in claim 1, characterized in that, The total energy cost of the factory is the sum of the cost of the photovoltaic panels, the cost of the energy storage cabinet, and the electricity cost after the intelligent scheduling strategy; by finding the minimum value of the total cost, the operating area of ​​the photovoltaic panels and the operating capacity of the energy storage cabinet are output.

4. The photovoltaic-storage power supply and intelligent dispatching method that comprehensively considers industrial production load and time-of-use pricing as described in claim 3, is characterized in that... The total monthly energy cost of the factory is obtained by converting the above costs to monthly costs. The calculation formula is as follows: ； ； in: This is the total monthly cost converted to the next month. This refers to the inverter cost calculated on a monthly basis. The number of inverters; Cost of a single converter; Time-of-use pricing for the main power grid; The amount of electricity supplied from the main power grid to the load.

5. A photovoltaic-storage power supply and intelligent dispatching device that comprehensively considers industrial production load and time-of-use electricity pricing, characterized in that, The system includes a front-end interface and a back-end interface. The back-end interface is equipped with a memory and a processor. The processor stores a program. When the processor loads the program, it executes the steps of the method described in any one of claims 1-4. With the goal of minimizing the total energy cost of the factory, it calculates the optimal values ​​of the photovoltaic panel operating area and the energy storage cabinet operating capacity, and outputs instructions to control the opening and closing of the photovoltaic panels at the photovoltaic operation site and the energy storage cabinet at the energy storage site to achieve reasonable scheduling. The front-end interface is used to collect user instructions, provide users with a visualized energy consumption scenario, and process and result data of calculation and control.

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