Agricultural machine mobile energy storage cooperation method and system for rural roof photovoltaic absorption

Abstract

The invention relates to an agricultural machinery mobile energy storage cooperation method and system for rural roof photovoltaic absorption. The method comprises the following steps: (1) battery role distinguishing and farming cycle identification; (2) calculating daily average energy consumption; (3) calculating roof photovoltaic day-by-day power generation capacity, and ensuring stable power supply of the system in a busy season through an electric power balance equation and a load loss probability model; (4) the economic benefit of the system is comprehensively calculated, and the cost structure is optimized; (5) optimizing a charging and discharging strategy, consuming photovoltaic power to the maximum extent, ensuring that an agricultural machinery battery effectively stores and releases power, and meeting the requirements of farmers and agricultural machinery; (6) based on the actual meteorological data and the agricultural machinery operation calendar, calculating a photovoltaic theoretical carbon reduction value and carrying out probability correction to eliminate evaluation deviation; and (7) balancing the economical efficiency, the photovoltaic power consumption rate and the reliability through a multi-objective optimization algorithm, and generating an optimal design scheme. According to the invention, the problem of space-time mismatching of photovoltaic consumption and agricultural machinery energy consumption in rural areas is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed energy management, and particularly relates to a rural machine mobile energy storage coordination method and system for rural roof photovoltaic consumption. BACKGROUND

[0002] At present, rural distributed photovoltaic power generation mainly adopts a "full on-grid" mode, that is, all the power generated by roof photovoltaic is connected to the power grid, resulting in operation pressure such as access difficulty and voltage fluctuation of the power grid in some areas. In order to alleviate the pressure of the power grid and improve the utilization efficiency of photovoltaic power generation, the policy and market gradually guide the rural users to change to the direction of "self-generation and self-use, and on-grid of surplus power", that is, to preferentially consume photovoltaic power locally, and only connect the remaining power to the power grid.

[0003] In order to improve the local consumption capacity of photovoltaic power generation and the system operation stability, a fixed energy storage system is generally used at present to store the excess power of photovoltaic and release it during the peak load period. However, the fixed energy storage system faces certain promotion difficulties in rural areas. On the one hand, the energy storage equipment itself has high cost, and the system configuration and installation process are complex, which brings great initial investment pressure to the farmers with limited economic conditions. On the other hand, the operation and maintenance of the energy storage system require high professional skills, and the technical service system in rural areas is not yet perfect, lacking of continuous operation and maintenance guarantee.

[0004] At the same time, with the development of agricultural electrification, a large number of agricultural machinery (such as electric tractors, electric transport vehicles, etc.) have been equipped with large-capacity power batteries. Due to the seasonality of agricultural work, these battery resources are mostly in idle state during the non-working period, and there is a significant redundant energy storage potential. Agricultural machinery has good mobility and distribution, and can flexibly go back and forth between the homestead and the farmland, and has the basic conditions to become a distributed mobile energy storage unit. Compared with traditional fixed energy storage, the agricultural machine battery has the economic advantages of having been invested in advance and being directly reusable, the space advantages of low land requirement and flexible deployment, and the operation advantages of supporting bidirectional charging and discharging, which can store electricity during the peak period of photovoltaic power generation, discharge electricity during the peak period of electricity consumption or the working period, realize efficient local consumption and space-time adjustment of electricity, and improve the local consumption capacity and overall energy efficiency of the distributed photovoltaic system. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a rural machine mobile energy storage coordination method and system for rural roof photovoltaic consumption; This invention proposes a photovoltaic-agricultural machinery energy storage synergistic system as a solution. This system uses the battery packs of agricultural machinery as mobile energy storage units, storing excess electricity generated by the rooftop photovoltaic system during idle periods and discharging it during agricultural machinery operation. This achieves local consumption of photovoltaic power, reduces dependence on the grid, and saves on electricity purchase costs. This synergistic system deeply integrates photovoltaic power generation and agricultural machinery energy storage, not only breaking through the bottlenecks of traditional energy storage utilization and optimizing the local consumption path of photovoltaic power, but also improving the energy utilization efficiency and flexible control capabilities of agricultural production, promoting the green and low-carbon transformation of rural energy structure, and contributing to the achievement of rural revitalization strategy goals. Specifically, this invention is used to improve the local consumption capacity of distributed photovoltaic power generation in rural areas, reduce the construction cost of energy storage systems, and optimize power utilization efficiency.

[0006] This invention, a mobile energy storage collaborative system for agricultural machinery, fully utilizes the large-capacity power battery resources idle during non-operation periods of agricultural machinery (such as electric tractors and electric transport vehicles), using them as mobile and reusable energy storage units. The system includes a photovoltaic output acquisition unit, a household load monitoring unit, an electricity price acquisition module, an agricultural machinery status sensing module, an energy storage management module, and a scheduling optimization module, enabling dynamic acquisition of photovoltaic power generation, electricity demand, and battery availability status. Based on this, the invention provides a method for dynamic supply and demand scheduling, comprehensively considering photovoltaic output characteristics, agricultural machinery operation cycles, electricity price peak-valley patterns, and load change trends to construct an electricity scheduling model and energy storage control strategy. This dynamically determines the charging and discharging behavior of agricultural machinery batteries, maximizing the local utilization of photovoltaic power. Specifically, during the off-season, the method intelligently identifies surplus photovoltaic periods and prioritizes charging idle agricultural machinery batteries. During busy seasons or peak load periods, the system schedules battery output according to a household load priority strategy, providing power support for agricultural machinery operations and household electricity consumption. This method also introduces multiple sub-models, such as energy storage lifecycle cost assessment, and uses multi-objective optimization algorithms (such as the improved NSGA-II) to jointly optimize key parameters. This invention has significant innovations in both system structure and methodological mechanism, balancing economic efficiency, reliability, and environmental benefits. It fully taps the energy storage potential of agricultural machinery power batteries, improves the local utilization rate of photovoltaic power, reduces dependence on the power grid, and is suitable for the construction of green energy systems and the modernization of agriculture in rural areas.

[0007] Terminology Explanation: 1. A photovoltaic energy storage system refers to a device installed in conjunction with (or independently of) a rooftop photovoltaic system to store excess electrical energy generated by photovoltaic power generation and release it when needed. It is a key buffer and regulating unit for addressing the intermittency of photovoltaic power generation and improving self-consumption rates. In agricultural machinery-mobile energy storage synergy methods, it plays the role of an energy hub.

[0008] 2. Rooftop photovoltaic systems refer to power generation devices installed on the roofs of rural houses, farmhouses, warehouses, or other agricultural buildings that directly convert solar energy into electrical energy. It is the energy source for the entire synergistic approach.

[0009] The technical solution of this invention is as follows: A method for integrating agricultural machinery with mobile energy storage for rural rooftop photovoltaic (PV) systems includes: (1) Battery role differentiation and agricultural cycle identification; Based on the agricultural operation calendar, the whole year is divided into the busy farming season and the slack farming season, and the functional status of the agricultural machinery battery is set; During the busy farming season, the agricultural machinery battery is only used as the power source for agricultural operations and does not participate in the scheduling of the photovoltaic energy storage system; During the slack farming season, the agricultural machinery battery serves as the energy storage unit of the rooftop photovoltaic system, storing the remaining photovoltaic power and supporting farmers' household electricity consumption or electricity price arbitrage. (2) Calculate the total area of ​​the load curve over the complete cycle, i.e. the total energy consumption, by integration, and then divide by the corresponding number of days to obtain the average daily energy consumption; (3) Calculate the daily power generation capacity of the rooftop photovoltaic system, and ensure stable power supply during the busy farming season by using the power balance equation and the load failure probability model; (4) Taking into account the initial investment, installation cost, operation and maintenance cost and energy storage revenue, calculate the economic benefits of the system comprehensively and optimize the cost structure; (5) Optimize the charging and discharging strategy to maximize the absorption of photovoltaic power and ensure that agricultural machinery batteries can effectively store and release power to meet the needs of farmers and agricultural machinery; (6) Based on actual meteorological data and agricultural machinery operation calendar, calculate the theoretical carbon reduction value of photovoltaics and make probability corrections to eliminate evaluation bias; (7) The optimal design scheme is generated by balancing economic efficiency, photovoltaic power consumption rate and reliability through a multi-objective optimization algorithm.

[0010] According to a preferred embodiment of the present invention, firstly, the agricultural machinery operation calendar is prepared. It can be expressed in the following form: (I); Then, the electricity demand of farm households and agricultural machinery is shown in equation (II): (II); This refers to the real-time power generation of rooftop photovoltaic systems. This is the net power of the agricultural machinery battery. >0 indicates discharge. <0 indicates charging; It is the power obtained from the power grid; and These are the electricity needs of farmers' households and agricultural machinery, respectively. The state of charge of the agricultural machinery energy storage battery changes over time during the charging and discharging process, as shown in equation (III): (III); In formula (III), It represents the state of charge of the battery at time t, indicating the absolute energy value; It is the state of charge of the battery at time t; It is the power transferred from the photovoltaic system to the battery. It is the power released from the battery to the load. and These are charging and discharging efficiency, respectively. This refers to the change over time; When photovoltaic power generation is insufficient, the agricultural machinery energy storage battery prioritizes providing electricity to farmers' households, as shown in equation (IV): (IV); In equation (IV), This is the net power of the agricultural machinery battery. This refers to the maximum power generation of rooftop solar panels; After the photovoltaic system meets the household's energy needs, it prioritizes charging the battery. If photovoltaic power generation is insufficient to meet the demand, the charging power is zero. If both photovoltaic and battery power cannot meet the demand, the grid will provide supplementary power, as shown in equation (V).

[0011] (V); In equation (V), This refers to the power transferred from the photovoltaic system to the battery. The power grid serves as the last resort power source, and the amount of electricity that needs to be supplemented to the grid is calculated, as shown in equation (VI): (VI); In formula (VI), It is the power obtained from the power grid.

[0012] According to a preferred embodiment of the present invention, the total area of ​​the load curve over a complete cycle, i.e., the total energy consumption, is calculated by integration, and then divided by the corresponding number of days to obtain the average daily energy consumption; specifically including: Collect and process daily hourly load curves to calculate daily load energy consumption: For each day d=1,2,…,D within the statistical period, collect hourly load curves Ld(t), where t represents the hour of the day. Daily load energy consumption is obtained through integration. ;in, This refers to the load at time t. This refers to the total duration. This refers to average daily energy consumption.

[0013] According to a preferred embodiment of the present invention, the power generation capacity of the rooftop photovoltaic system is calculated, and a stable power supply is ensured during the busy farming season through power balance equations and a load shedding probability model; including: Assuming the total roof area is A square meters, the installation utilization rate is... Then the total usable area for: (VII); Estimating the annual power generation E, i.e., the power generation capacity of rooftop photovoltaic systems: (VIII); Where S is the solar radiation intensity. The total efficiency of the rooftop photovoltaic system; Represented as: (IX); The parameters include: Photovoltaic module efficiency This refers to the efficiency of a photovoltaic module in converting solar energy into electrical energy under ideal conditions. Photovoltaic module aging coefficient This refers to the percentage decrease in photovoltaic module efficiency over the years of use; as shown below: (X); in, Refers to the component efficiency in the first year. It is the initial photovoltaic system efficiency. This is the aging factor of photovoltaic modules in their first year. This is the aging factor of subsequent components, where t represents the year. This represents the component efficiency in year n. Inverter efficiency This refers to the efficiency of an inverter in converting direct current (DC) to alternating current (AC). Pollution loss This refers to the percentage of efficiency loss due to surface contamination. Ground reflectivity , refers to: physical parameters that describe the Earth's surface's ability to reflect solar radiation; Obtain rooftop photovoltaic power generation Then, the probability of load failure (LOLP) is calculated: (XI); (XII); (XIII); in, , , , , , , These refer to power supply capacity, power consumption, maximum battery load capacity, total battery capacity, grid load, total user load capacity, and agricultural machinery operation capacity, respectively. This is an indicator function, and the charging / discharging power constraints are as follows: (XIV); (XV); in, It refers to battery discharge efficiency. It is the state of charge of the battery at time t.

[0014] According to a preferred embodiment of the present invention, the economic benefits of the system are comprehensively calculated, taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, and the cost structure is optimized; including: Initial investment and maintenance costs On this basis, increase energy storage revenue The true net cost (LCC) is calculated through peak-valley arbitrage and recovery from wasted solar power; specifically including: The true net cost (LCC) is composed of the following: (XVI); Among them, initial investment This includes the initial cost of installing a photovoltaic system, including equipment, installation, insurance, and other upfront expenses; and operation and maintenance costs. This includes annual system maintenance and operating costs, calculated at 1% of the total investment cost; For equipment residual value, For the discount rate, The system lifespan is in years. Energy storage revenue model as follows: (XVII); in, This is the discharge amount on day d, in kWh; Peak-valley electricity price difference, ¥ / kWh; The waste light recovery factor is 0.6-0.8; Total load power, kW; The economic benefit of the system is to minimize the true net cost (LCC).

[0015] According to a preferred embodiment of the present invention, an optimal design scheme is generated by balancing economic efficiency, photovoltaic power consumption rate and reliability through a multi-objective optimization algorithm. Decision variables As shown below: (XXI); in, It is the peak power of rooftop photovoltaic systems. It refers to the capacity of agricultural machinery batteries. It is the off-peak charging strategy vector; The objective function is as follows: (XXII); in, The photovoltaic grid integration rate is specifically calculated as follows: ;in, This indicates the total power generation from the rooftop solar photovoltaic system. This indicates the amount of photovoltaic power generated locally; The objectives are to minimize the true net cost, maximize the photovoltaic power consumption rate, and minimize the risk of power outages, i.e., minimize the probability of load shedding. All constraints include: (XXIII); h -1 (XXIV); (XXV); (XXVI); Formula (XXIII) indicates that the probability of power outages during the busy farming season is no greater than 5%. Formula (XXIV) indicates that the battery's SOC change rate per hour is no more than 20%. This refers to the change in SOC; The formula (XXV) indicates that the power purchased by the power grid is within a safe range; Formula (XXVI) means: Ensure that the photovoltaic power absorption rate is not less than 45%; Finally, the evaluation is performed using the normalized comprehensive evaluation function, as shown below: (XXVII); in, This represents the overall score; w1, w2, and w3 are all weighting coefficients. This refers to the maximum value of LCC(x) among all candidate solutions; This refers to the minimum value of LCC(x) among all candidate solutions; This refers to the maximum value of PVAR(x) among all candidate solutions; This refers to the minimum value of PVAR(x) among all candidate solutions; This refers to the maximum value of LOLP(x) among all candidate solutions; This refers to the minimum value of LOLP(x) among all candidate solutions; The solution with the highest overall score is recommended as the optimal design.

[0016] A mobile energy storage and collaborative system for agricultural machinery to absorb rooftop photovoltaic power in rural areas includes: The agricultural cycle identification module is configured to: divide the year into busy and slack seasons based on the agricultural operation calendar, and set the functional status of the agricultural machinery battery; during the busy season, the agricultural machinery battery is used only as a power source for agricultural operations and does not participate in the scheduling of the photovoltaic energy storage system; during the slack season, the agricultural machinery battery serves as an energy storage unit for the rooftop photovoltaic system, storing the remaining photovoltaic power and supporting household electricity consumption or electricity price arbitrage. The daily average energy consumption calculation module is configured to calculate the total area of ​​the load curve over the entire cycle, i.e., the total energy consumption, by integration, and then divide by the corresponding number of days to obtain the daily average energy consumption. The photovoltaic power supply capacity calculation module is configured to calculate the daily power generation capacity of the rooftop photovoltaic system, and ensure stable power supply during the busy farming season by using power balance equations and load failure probability models. The economic benefit calculation module is configured to comprehensively calculate the economic benefits of the system, taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, and optimize the cost structure. The charging and discharging strategy optimization module is configured to: optimize the charging and discharging strategy, maximize the absorption of photovoltaic power, and ensure that the agricultural machinery battery effectively stores and releases power to meet the needs of farmers and agricultural machinery; The carbon reduction value correction module is configured to: calculate the theoretical carbon reduction value of photovoltaics and perform probability correction based on actual meteorological data and agricultural machinery operation calendar to eliminate evaluation bias; The multi-objective optimization module is configured to balance economic efficiency, photovoltaic power consumption rate, and reliability through a multi-objective optimization algorithm to generate the optimal design scheme.

[0017] The beneficial effects of this invention are as follows: This invention addresses the spatiotemporal mismatch between solar photovoltaic (PV) consumption and agricultural machinery energy use in rural areas by proposing an evaluation method for a rooftop PV-agricultural machinery energy storage synergistic system. By constructing a multi-level evaluation model, combined with a model maximizing local PV consumption, minimizing total lifecycle costs, and considering safety constraints, the method effectively improves equipment utilization and reduces energy costs. This evaluation method provides a standardized decision-making basis for the design of rural PV-energy storage systems, enabling rural areas to utilize renewable energy more efficiently, promoting the popularization and application of zero-carbon energy, thereby driving sustainable development in rural areas, improving energy efficiency, and reducing dependence on traditional energy sources. Attached Figure Description

[0018] Figure 1This is a flowchart illustrating a collaborative method for agricultural machinery and mobile energy storage to integrate rooftop photovoltaic power generation in rural areas. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments, but is not limited thereto.

[0020] Example 1 A method for integrating agricultural machinery with mobile energy storage for rural rooftop photovoltaic (PV) systems, such as... Figure 1 As shown, it includes: This invention designs a rooftop photovoltaic-agricultural machinery energy storage collaborative system and its evaluation method. The system design clearly distinguishes the usage of agricultural machinery batteries during busy and off-seasons to avoid impacting agricultural operational efficiency due to their involvement in energy storage. During busy seasons (preset by the agricultural work calendar), agricultural machinery batteries are dedicated to field operations and do not participate in household load scheduling or the photovoltaic energy storage system. During off-seasons, agricultural machinery batteries participate in scheduling as energy storage units of the rooftop photovoltaic system, used to store excess photovoltaic power, peak shaving, and supplement household electricity consumption. Specific steps are as follows:

[0021] (1) Battery role differentiation and agricultural cycle identification; Based on the agricultural operation calendar, the whole year is divided into the busy farming season and the slack farming season, and the functional status of the agricultural machinery battery is set; During the busy farming season, the agricultural machinery battery is only used as the power source for agricultural operations and does not participate in the scheduling of the photovoltaic energy storage system to ensure uninterrupted operation of agricultural machinery; During the slack farming season, the agricultural machinery battery serves as the energy storage unit of the rooftop photovoltaic system, storing the remaining photovoltaic power and supporting household electricity use or electricity price arbitrage; improving energy utilization efficiency; (2) Calculate the total area of ​​the load curve over the complete cycle, i.e. the total energy consumption, by integration, and then divide by the corresponding number of days to obtain the average daily energy consumption; (3) Calculate the daily power generation capacity of the rooftop photovoltaic system, and ensure stable power supply during the busy farming season by using the power balance equation and the probability of load loss (LOLP) model; meet the high load demand.

[0022] (4) Taking into account the initial investment, installation cost, operation and maintenance cost and energy storage revenue, calculate the economic benefits of the system comprehensively and optimize the cost structure; (5) Optimize the charging and discharging strategy to maximize the absorption of photovoltaic power and ensure that agricultural machinery batteries can effectively store and release power to meet the needs of farmers and agricultural machinery; (6) Based on actual meteorological data and agricultural machinery operation calendar, calculate the theoretical carbon reduction value of photovoltaics and make probability corrections to eliminate evaluation bias; (7) By balancing economic efficiency, photovoltaic power consumption rate and reliability through a multi-objective optimization algorithm, the optimal design scheme is generated to ensure the long-term sustainability of the system.

[0023] Example 2 The difference between the method for agricultural machinery-mobile energy storage collaboration for rural rooftop photovoltaic power consumption described in Example 1 and the method described in Example 1 is as follows: First, the agricultural machinery operation calendar It can be expressed in the following form: (I); Then, to achieve efficient coordination between rural farmers' energy needs and agricultural machinery energy storage, agricultural machinery not only provides power for agriculture but also acts as an energy storage unit for rooftop photovoltaic systems. It stores excess photovoltaic power during the off-season and releases it during the busy season to support agricultural operations. Farmers' household energy needs will also be met, with priority given to supplying power through agricultural machinery energy storage batteries when photovoltaic power is insufficient.

[0024] The electricity demand of farm households and agricultural machinery is shown in equation (II): (II); Equation (II) indicates that the electricity demand at any given moment (for farm households and agricultural machinery) is met by three sources of electricity: rooftop photovoltaic power generation, electricity provided by the energy storage batteries of agricultural machinery, and electricity from the power grid; This refers to the real-time power generation of rooftop photovoltaic systems. This is the net power of the agricultural machinery battery. >0 indicates discharge. <0 indicates charging; It is the power obtained from the power grid; and These are the electricity needs of farmers' households and agricultural machinery, respectively. The state of charge (SOC) of the agricultural machinery energy storage battery changes over time during the charging and discharging process, as shown in Equation (III): (III); In formula (III), It represents the state of charge of the battery at time t, indicating the absolute energy value; It is the state of charge of the battery at time t; It is the power transferred from the photovoltaic system to the battery. It is the power released from the battery to the load. and These are charging and discharging efficiency, respectively. This refers to the change over time; When photovoltaic power generation is insufficient, the agricultural machinery energy storage battery prioritizes providing electricity to farmers' households, as shown in equation (IV): (IV); In equation (IV), This is the net power of the agricultural machinery battery. This refers to the maximum power generation of rooftop solar panels; After the photovoltaic system meets the household's energy needs, it prioritizes charging the battery. If photovoltaic power generation is insufficient to meet the demand, the charging power is zero. If both photovoltaic and battery power cannot meet the demand, the grid will provide supplementary power, as shown in equation (V).

[0025] (V); In equation (V), This refers to the power transferred from the photovoltaic system to the battery. The power grid serves as the last resort power source, and the amount of electricity that needs to be supplemented to the grid is calculated, as shown in equation (VI): (VI); In formula (VI), It is the power obtained from the power grid.

[0026] The total area of ​​the load curve over a complete cycle, i.e., the total energy consumption, is calculated by integration, and then divided by the corresponding number of days to obtain the average daily energy consumption; specifically including: Collect and process daily hourly load curves to calculate daily load energy consumption: For each day within the statistical period d=1,2,…,D (usually D=365), collect hourly load curves Ld(t), where t represents the hour of the day (0–24h). Daily load energy consumption is obtained through integration. ;in, This refers to the load at time t. This refers to the total duration (in hours). This refers to the average daily energy consumption (kWh / day).

[0027] The power generation capacity of rooftop photovoltaic systems is calculated, and the power balance equation and probability of load shedding (LOLP) model are used to ensure stable power supply during the busy farming season; this includes: By incorporating a reliability constraint model, the power generation capacity of rural rooftops is first calculated, and then the power supply capacity (photovoltaics + batteries + grid ≥ load + agricultural machinery) is verified in real time. The probability of load failure (LOLP) is also calculated to ensure uninterrupted power supply for high-power operations (such as irrigation) during the busy farming season, which does not affect the normal use of agricultural machinery.

[0028] Assuming the total roof area is A square meters, the installation utilization rate is... (For north and south roofs, install flat; for horizontal roofs, install at the optimal upward angle; for east and west roofs, do not install), then the total usable area is... for: (VII); Estimating the annual power generation E, i.e., the power generation capacity of rooftop photovoltaic systems: (VIII); Where S is the solar radiation intensity. The total efficiency of the rooftop photovoltaic system; Represented as: (IX); The parameters include: Photovoltaic module efficiency This refers to the efficiency of a photovoltaic module in converting solar energy into electrical energy under ideal conditions; National Standard No.: GB / T 39857-2021; Photovoltaic module aging coefficient This refers to the percentage decrease in photovoltaic module efficiency over the years of use; as shown below: (X); in, Refers to the component efficiency in the first year. It is the initial photovoltaic system efficiency. This is the aging factor of photovoltaic modules in their first year. This is the aging factor of subsequent components, where t represents the year. This represents the component efficiency in year n. Photovoltaic module efficiency = 17%; the aging coefficient of photovoltaic modules is 2.5% in the first year and 0.7% thereafter.

[0029] Inverter efficiency This refers to the efficiency of the inverter in converting direct current (DC) to alternating current (AC); the average efficiency of inverters currently on the market is taken as 98%.

[0030] Pollution loss This refers to the percentage of efficiency loss due to surface contamination; this invention primarily considers rural areas, so a larger value is used. 5%.

[0031] Ground reflectivity This refers to a physical parameter describing the Earth's surface's ability to reflect solar radiation; based on literature research, it is taken as 10%.

[0032] Obtain rooftop photovoltaic power generation Then, the probability of load failure (LOLP) is calculated: (XI); (XII); (XIII); in, , , , , , , These refer to power supply capacity, power consumption, maximum battery load capacity, total battery capacity, grid load, total user load capacity, and agricultural machinery operation capacity, respectively. It is an indicator function (1 when the condition is met), and the charging and discharging power constraints are as follows: (XIV); (XV); in, It refers to the battery discharge efficiency (0.85-0.95). It represents the state of charge of the battery at time t.

[0033] The power balance equation is equation (II).

[0034] Taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, the economic benefits of the system are comprehensively calculated, and the cost structure is optimized; including: To calculate the total lifecycle cost, at the initial investment and maintenance costs On this basis, increase energy storage revenue The true net cost (LCC) of mobile energy storage is calculated through peak-valley arbitrage (agricultural machinery batteries charging during off-peak hours and discharging during peak hours to profit from the price difference) and curtailment recovery (storing excess photovoltaic power to replace high-priced grid electricity); this reflects the economic value of mobile energy storage. Specifically, this includes:

[0035] The true net cost (LCC) is composed of the following: (XVI); Among them, initial investment This includes the initial cost of installing a photovoltaic system, including equipment, installation, insurance, and other upfront expenses; and operation and maintenance costs. This includes annual system maintenance and operating costs, calculated at 1% of the total investment cost; For equipment residual value, For the discount rate, The system lifespan is in years. Energy storage revenue model as follows: (XVII); in, This is the discharge amount on day d, in kWh; Peak-valley electricity price difference, ¥ / kWh; The waste light recovery factor is 0.6-0.8; Total load power, kW; The system's economic benefit is to minimize the true net cost (LCC). This cost is accurately assessed by dynamically quantifying the offsetting effect of energy storage revenue on the total cost. The LCC calculation model integrates the initial investment cost (including photovoltaic equipment, installation, and insurance costs), operation and maintenance costs (discounted at 1% of the annual investment), energy storage revenue (including peak-valley electricity price arbitrage revenue and revenue from the recovery of curtailed photovoltaic power), and equipment residual value, and discounts the net present value over the entire lifecycle using a discount rate.

[0036] Optimizing the cost structure refers to dynamically adjusting decision variables (PV capacity, battery capacity, and off-peak charging strategy) through a multi-objective optimization algorithm to reconstruct the proportional relationship of various economic factors: increasing PV capacity can increase the revenue from curtailment recovery and reduce reliance on electricity purchases during periods of high electricity prices; expanding battery capacity can enhance peak-valley arbitrage revenue and offset the increase in initial investment; optimizing the charging strategy can maximize the efficiency of energy storage revenue generation. Under the premise of satisfying reliability constraints (LOLP ≤ 5%), the algorithm balances the marginal revenue and marginal cost of PV / energy storage, reduces the net present value ratio of initial investment and operation and maintenance costs, and increases the deduction ratio of energy storage revenue, ultimately minimizing the total lifecycle net cost (LCC) and completing the systematic optimization of the cost structure.

[0037] Optimize charging and discharging strategies to maximize local absorption of photovoltaic power and ensure effective storage and release of electricity by agricultural machinery batteries, meeting the needs of farmers and agricultural machinery; including: a. Data input preparation; This includes obtaining photovoltaic power output forecast curves (24-hour power generation), load demand curves (electricity consumption for farmers' domestic use and agricultural machinery operations), and marking the power grid; Off-peak electricity hours (such as 00:00-07:00) and peak electricity hours, etc.; b. Taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, comprehensively calculate the economic benefits of the system and optimize the cost structure; c. Charging strategy generation; Solar power surplus charging: When the real-time photovoltaic power exceeds the load demand, charging is automatically started to prioritize the consumption of excess photovoltaic power.

[0038] Off-peak charging: During preset off-peak electricity price periods, if the battery is not fully charged, grid charging will be initiated to replenish the capacity.

[0039] d. Discharge strategy control; Discharge during power supply gap periods: When photovoltaic power generation cannot meet load demand, the battery discharge is started immediately.

[0040] Active discharge during peak electricity hours: During periods of high electricity prices, even if photovoltaic power can meet the load, some power is still discharged to replace grid-purchased electricity in order to earn the price difference.

[0041] e. Key safeguard mechanisms; Battery protection limit: real-time adjustment of charging and discharging power to ensure that the battery charge change per hour does not exceed 20% of the total capacity.

[0042] Prioritize power supply during busy farming seasons: During peak farming seasons, power demand is not affected by economic policies, and the power supply for agricultural machinery is guaranteed.

[0043] Grid interaction safety: The power purchased from the grid is strictly controlled between zero and the maximum allowable value to avoid overload.

[0044] f. Enhanced photovoltaic power absorption; Set dynamic monitoring thresholds: When photovoltaic power generation exceeds load demand, automatically trigger a charging command, deduct only the fixed loss ratio (such as 30%), and ensure that more than 90% of the surplus photovoltaic power is stored and utilized.

[0045] g. Agricultural machinery demand response; Check the battery level one hour before the peak of agricultural machinery operation. If the power supply is expected to be insufficient, start the reserve discharge mode in advance.

[0046] h. Strategy optimization closed loop; By improving the NSGA-II algorithm, global optimization is performed on the binary sequence of the 24-hour charge-discharge strategy.

[0047] The optimal design scheme is generated by balancing economic efficiency, photovoltaic power consumption rate and reliability through a multi-objective optimization algorithm. After completing the above calculations, the three main objectives can be optimized synergistically: maximizing photovoltaic power absorption rate (improving the efficiency of new energy utilization), minimizing the probability of load shedding (ensuring power supply reliability and reducing the risk of power outages), and maximizing economic benefits (reducing the total life cycle cost and achieving economic optimization). Candidate solutions are generated by adjusting photovoltaic capacity, battery capacity, and off-peak charging strategies, and finally ranked according to comprehensive scores. A list of technology priorities is output, providing a decision-making basis for village and town planning that balances cost, environmental protection, and stability. Specifically, this includes:

[0048] Decision variables As shown below: (XXI); in, It is the peak power of rooftop photovoltaic systems. It refers to the capacity of agricultural machinery batteries. It is the off-peak charging strategy vector (1 when off-peak charging is performed in time period t, and 0 in other time periods). The objective function is as follows: (XXII); in, The photovoltaic grid integration rate is specifically calculated as follows: ;in, This indicates the total power generation from the rooftop solar photovoltaic system. This indicates the amount of photovoltaic power generated locally; , , It is what was obtained above. , , .

[0049] The objectives are to minimize the true net cost, maximize the photovoltaic power consumption rate, and minimize the risk of power outages, i.e., minimize the probability of load shedding. All constraints include: (XXIII); h -1 (XXIV); (XXV); (XXVI); Formula (XXIII) means: the probability of power outages during the busy farming season is no greater than 5%; ensuring the normal operation of agricultural production; Formula (XXIV) indicates that the battery's SOC change rate per hour is no more than 20%. This refers to the change in SOC; The formula (XXV) indicates that the power purchased by the power grid is within a safe range; Formula (XXVI) means: ensure that the photovoltaic absorption rate is not less than 45%; avoid waste of new energy resources.

[0050] Finally, the evaluation is performed using the normalized comprehensive evaluation function, as shown below: (XXVII); in, This represents the overall score; w1, w2, and w3 are all weighting coefficients. It refers to the maximum value of LCC(x) among all candidate solutions (the cost of the highest cost solution); It refers to the minimum value of LCC(x) among all candidate solutions (the cost of the lowest cost solution); It refers to the maximum value of PVAR(x) among all candidate schemes (the absorption ratio of the scheme with the highest local absorption rate). It refers to the minimum PVAR(x) among all candidate schemes (the local absorption rate of the scheme with the lowest absorption rate). This refers to the maximum value of LOLP(x) among all candidate solutions (the risk of the solution with the highest risk of power outage). It refers to the minimum value of LOLP(x) among all candidate solutions (the risk of the solution with the lowest power outage risk); An improved NSGA-II multi-objective optimization algorithm is used to solve the problem, and the solution with the highest comprehensive score is recommended as the optimal design.

[0051] The improved NSGA-II multi-objective optimization algorithm is used to solve the problem; including: This invention addresses the multi-objective optimization problem of rural rooftop photovoltaic-agricultural machinery battery energy storage systems (simultaneously minimizing the total lifecycle cost (LCC), maximizing the photovoltaic power absorption rate (PVAR), and minimizing the power outage probability (LOLP)). An improved NSGA-II algorithm is employed for this problem. The specific solution process is as follows:

[0052] First, the decision variables (peak photovoltaic power, battery capacity, and binary vector of 24-hour off-peak charging strategy) are encoded as population individuals, and initial candidate schemes are randomly generated. For each initial candidate solution, system simulation is performed to calculate the objective function (XXII) and verify the constraints. Infeasible solutions are automatically repaired through a feasibility rule priority mechanism. Subsequently, non-dominated sorting is performed: the population is divided into Pareto front ranks according to the dominance of the solutions (if a solution is not inferior to another solution on all objectives and is strictly superior to at least one objective, then it constitutes dominance), and the crowding distance of solutions within each front is calculated to evaluate the solution set distribution density. Parents are selected based on a binary tournament selection method (prioritizing individuals with high frontier levels and then those with high crowding). Offspring are generated using a segmented crossover operator (multi-point crossover is used to protect key strategies during busy farming seasons, and uniform crossover is used to enhance diversity during non-busy farming seasons) and a dynamic mutation strategy (small perturbations to continuous variables and directional mutations of discrete variables based on electricity price sensitivity).

[0053] After merging the parent and offspring generations, elite individuals are retained through non-dominated sorting and crowding calculation to form a new generation of population. This process is iterated until convergence. The final output is a Pareto optimal solution set that satisfies all constraints, providing diverse alternatives for system design. Users can select the optimal design scheme based on specific application requirements and applicable decision-making methods.

[0054] The above model constructs a rooftop photovoltaic-agricultural machinery energy storage collaborative system and provides a quantitative evaluation method. Through dynamic correction factors, full life cycle cost modeling and multi-objective optimization, it solves the problem of energy mismatch in time and space in rural areas.

[0055] Take a village in a certain province as an example; Number of households: 50, each with a rooftop photovoltaic installed capacity of 10 kW (average daily power generation ≈ 35 kWh, total daily power generation of the whole village is 1750 kWh). Agricultural machinery configuration: 20 electric tractors (each with a battery capacity of 100 kWh and a charging and discharging power of 20 kW), and 10 electric transport vehicles (each with a battery capacity of 50 kWh). Electricity pricing policy: Peak-valley electricity pricing (peak electricity 1.2 yuan / kWh, valley electricity 0.3 yuan / kWh); Agricultural Calendar: During the busy farming season (May-June, September-October): Agricultural machinery operates in the fields for 8 hours a day, with batteries prioritizing power supply for agricultural use; Off-season for farming (November to April of the following year): Agricultural machinery is idled, and batteries are used for household load scheduling; System operation data (typical days during the off-season) is shown in Table 1: Table 1

[0056] Photovoltaic grid integration rate: 92% (the unintegrated portion is due to the batteries being fully charged); Peak-valley arbitrage profit: Utilizing off-peak electricity to charge 150 kWh and peak electricity to discharge 200 kWh, the daily profit is 180 yuan (price difference of 0.9 yuan / kWh × 200 kWh). Curtailment recovery capacity: 40 kWh (through battery storage to replace expensive grid power); The annual benefit data comparison is shown in Table 2: Table 2

[0057] This system innovatively utilizes idle agricultural machinery batteries (such as electric tractors and transport vehicles) as mobile energy storage units, forming a collaborative network with rooftop photovoltaic systems. Through a dynamic scheduling mechanism driven by the agricultural calendar (prioritizing agricultural machinery operation during busy seasons and switching to household energy storage units during off-seasons), it achieves optimized spatial and temporal allocation of photovoltaic power. In a case study in a village in Shandong (50 households, 30 electric agricultural machines), the system demonstrated three core advantages:

[0058] 1. Economic breakthrough; Reusing agricultural machinery batteries reduces initial investment by 35%. Combined with peak-valley electricity price arbitrage (daily profit of 180 yuan) and the recovery of curtailed solar power, the annual operating cost is only 781,000 yuan, saving 18.2% compared to fixed energy storage systems. Under local subsidy policies, the investment payback period is shortened to 4.5 years.

[0059] 2. Reduce carbon emissions and increase efficiency; The Dynamically Corrected Carbon Reduction (DCR) model accurately quantifies the impact of weather and operational cycles, achieving an annual CO2 reduction of 89.7 tons, a 43.5% improvement compared to systems without energy storage. The photovoltaic grid integration rate jumped from 68% to 92%, effectively alleviating grid pressure.

[0060] 3. Reliable operation; The risk of power outage during the busy farming season (LOLP) is strictly controlled at 2% (below the 5% safety threshold). This is achieved through the limit on the rate of change of battery SOC (|∇SOC|≤0.2 h⁻¹) and the constraints on power purchase from the grid, ensuring the continuity of agricultural operations and the safety of electricity use.

[0061] This system deeply integrates agricultural production and energy management, providing replicable green energy solutions for regions with abundant sunshine resources and high levels of agricultural mechanization (such as rural revitalization demonstration zones in Ningxia and Gansu).

[0062] Example 3 A mobile energy storage and collaborative system for agricultural machinery to absorb rooftop photovoltaic power in rural areas includes: The agricultural cycle identification module is configured to: divide the year into busy and slack seasons based on the agricultural operation calendar, and set the functional status of agricultural machinery batteries; during the busy season, the agricultural machinery batteries are used only as power sources for agricultural operations and do not participate in the scheduling of the photovoltaic energy storage system to ensure uninterrupted operation of agricultural machinery; during the slack season, the agricultural machinery batteries serve as energy storage units for the rooftop photovoltaic system, storing surplus photovoltaic power and supporting household electricity consumption or electricity price arbitrage for farmers; thereby improving energy utilization efficiency. The daily average energy consumption calculation module is configured to calculate the total area of ​​the load curve over the entire cycle, i.e., the total energy consumption, by integration, and then divide by the corresponding number of days to obtain the daily average energy consumption. The photovoltaic power supply capacity calculation module is configured to calculate the daily power generation capacity of rooftop photovoltaics, using power balance equations and the probability of load failure (LOLP) model to ensure stable power supply during the busy farming season and meet high load demands.

[0063] The economic benefit calculation module is configured to comprehensively calculate the economic benefits of the system, taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, and optimize the cost structure. The charging and discharging strategy optimization module is configured to: optimize the charging and discharging strategy, maximize the absorption of photovoltaic power, and ensure that the agricultural machinery battery effectively stores and releases power to meet the needs of farmers and agricultural machinery; The carbon reduction value correction module is configured to: calculate the theoretical carbon reduction value of photovoltaics and perform probability correction based on actual meteorological data and agricultural machinery operation calendar to eliminate evaluation bias; The multi-objective optimization module is configured to balance economic efficiency, photovoltaic power consumption rate, and reliability through a multi-objective optimization algorithm to generate the optimal design scheme, ensuring the long-term sustainability of the system.

Claims

1. A method for coordinating agricultural machinery with mobile energy storage for rural rooftop photovoltaic (PV) absorption, characterized in that, include: (1) Battery role differentiation and agricultural cycle identification; Based on the agricultural operation calendar, the whole year is divided into the busy farming season and the slack farming season, and the functional status of the agricultural machinery battery is set; During the busy farming season, the agricultural machinery battery is only used as the power source for agricultural operations and does not participate in the scheduling of the photovoltaic energy storage system; During the slack farming season, the agricultural machinery battery serves as the energy storage unit of the rooftop photovoltaic system, storing the remaining photovoltaic power and supporting farmers' household electricity consumption or electricity price arbitrage. (2) Calculate the total area of ​​the load curve over the complete cycle, i.e. the total energy consumption, by integration, and then divide by the corresponding number of days to obtain the average daily energy consumption; (3) Calculate the daily power generation capacity of the rooftop photovoltaic system, and ensure stable power supply during the busy farming season by using the power balance equation and the load failure probability model; (4) Taking into account the initial investment, installation cost, operation and maintenance cost and energy storage revenue, calculate the economic benefits of the system comprehensively and optimize the cost structure; (5) Optimize the charging and discharging strategy to maximize the absorption of photovoltaic power and ensure that agricultural machinery batteries can effectively store and release power to meet the needs of farmers and agricultural machinery; (6) Based on actual meteorological data and agricultural machinery operation calendar, calculate the theoretical carbon reduction value of photovoltaics and make probability corrections to eliminate evaluation bias; (7) The optimal design scheme is generated by balancing economic efficiency, photovoltaic power consumption rate and reliability through a multi-objective optimization algorithm.

2. The method for coordinated agricultural machinery and mobile energy storage for rural rooftop photovoltaic power consumption according to claim 1, characterized in that, First, the agricultural machinery operation calendar It can be expressed in the following form: （I）； Then, the electricity demand of farm households and agricultural machinery is shown in equation (II): (II); This refers to the real-time power generation of rooftop photovoltaic systems. This is the net power of the agricultural machinery battery. >0 indicates discharge. <0 indicates charging; It is the power obtained from the power grid; and These are the electricity needs of farmers' households and agricultural machinery, respectively. The state of charge of the agricultural machinery energy storage battery changes over time during the charging and discharging process, as shown in equation (III): (III); In formula (III), It represents the state of charge of the battery at time t, indicating the absolute energy value; It is the state of charge of the battery at time t; It is the power transferred from the photovoltaic system to the battery. It is the power released from the battery to the load. and These are charging and discharging efficiency, respectively. This refers to the change over time; When photovoltaic power generation is insufficient, the agricultural machinery energy storage battery prioritizes providing electricity to farmers' households, as shown in equation (IV): (IV); In equation (IV), This is the net power of the agricultural machinery battery. This refers to the maximum power generation of rooftop solar panels; After the photovoltaic system meets the household's energy needs, it prioritizes charging the battery. If the photovoltaic power generation is insufficient to meet the demand, the charging power is zero. If the photovoltaic system and the battery cannot meet the demand, the grid will provide supplementary power, as shown in equation (V). （V）； In equation (V), This refers to the power transferred from the photovoltaic system to the battery. The power grid serves as the last resort power source, and the amount of electricity that needs to be supplemented to the grid is calculated, as shown in equation (VI): (WE); In formula (VI), It is the power obtained from the power grid.

3. The method for coordinated agricultural machinery and mobile energy storage for rural rooftop photovoltaic power consumption according to claim 1, characterized in that, The total area of ​​the load curve over a complete cycle, i.e., the total energy consumption, is calculated by integration, and then divided by the corresponding number of days to obtain the average daily energy consumption; specifically including: Collect and process daily hourly load curves to calculate daily load energy consumption: For each day d=1,2,…,D within the statistical period, collect hourly load curves Ld(t), where t represents the hour of the day. Daily load energy consumption is obtained through integration. ;in, This refers to the load at time t. This refers to the total duration. This refers to average daily energy consumption.

4. The method for coordinated agricultural machinery and mobile energy storage for rural rooftop photovoltaic power consumption according to claim 1, characterized in that, Calculate the power generation capacity of rooftop photovoltaic systems, and ensure stable power supply during the busy farming season through power balance equations and load shedding probability models; including: Assuming the total roof area is A square meters, the installation utilization rate is... Then the total usable area for: (VII); Estimating the annual power generation E, i.e., the power generation capacity of rooftop photovoltaic systems: (VIII); Where S is the solar radiation intensity. The total efficiency of the rooftop photovoltaic system; Represented as: (IX); The parameters include: Photovoltaic module efficiency This refers to the efficiency of a photovoltaic module in converting solar energy into electrical energy under ideal conditions. Photovoltaic module aging coefficient This refers to the percentage decrease in photovoltaic module efficiency over the years of use; as shown below: （X）； in, Refers to the component efficiency in the first year. It is the initial photovoltaic system efficiency. This is the aging factor of photovoltaic modules in their first year. This is the aging factor of subsequent components, where t represents the year. This represents the component efficiency in year n. Inverter efficiency This refers to the efficiency of an inverter in converting direct current (DC) to alternating current (AC). Pollution loss This refers to the percentage of efficiency loss due to surface contamination. Ground reflectivity , refers to: physical parameters that describe the Earth's surface's ability to reflect solar radiation; Obtain rooftop photovoltaic power generation Then, the probability of load failure (LOLP) is calculated: (XI); (XII); (XIII); in, , , , , , , These refer to power supply capacity, power consumption, maximum battery load capacity, total battery capacity, grid load, total user load capacity, and agricultural machinery operation capacity, respectively. This is an indicator function, and the charging / discharging power constraints are as follows: (XIV); (XV); in, It refers to battery discharge efficiency. It is the state of charge of the battery at time t.

5. A method for coordinating agricultural machinery with mobile energy storage for rural rooftop photovoltaic power generation, as described in claim 1, is characterized in that... Taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, the economic benefits of the system are comprehensively calculated, and the cost structure is optimized; including: Initial investment and maintenance costs On this basis, increase energy storage revenue The true net cost (LCC) is calculated through peak-valley arbitrage and recovery from wasted solar power; specifically including: The true net cost (LCC) is composed of the following: (XVI); Among them, initial investment This includes the initial cost of installing a photovoltaic system, including equipment, installation, insurance, and other upfront expenses; and operation and maintenance costs. This includes annual system maintenance and operating costs, calculated at 1% of the total investment cost; For equipment residual value, For the discount rate, The system lifespan is in years. Energy storage revenue model as follows: (XVII); in, This is the discharge amount on day d, in kWh; Peak-valley electricity price difference, ¥ / kWh; The waste light recovery factor is 0.6-0.8; Total load power, kW; The economic benefit of the system is to minimize the true net cost (LCC).

6. A method for coordinating agricultural machinery with mobile energy storage for rural rooftop photovoltaic power generation, as described in claims 1-5, is characterized in that... The optimal design scheme is generated by balancing economic efficiency, photovoltaic power consumption rate and reliability through a multi-objective optimization algorithm. Decision variables As shown below: (XXI); in, It is the peak power of rooftop photovoltaic systems. It refers to the capacity of agricultural machinery batteries. It is the off-peak charging strategy vector; The objective function is as follows: (XXII); in, The photovoltaic grid integration rate is specifically calculated as follows: ;in, This indicates the total power generation from the rooftop solar photovoltaic system. This indicates the amount of photovoltaic power generated locally; The objectives are to minimize the true net cost, maximize the photovoltaic power consumption rate, and minimize the risk of power outages, i.e., minimize the probability of load shedding. All constraints include: (XXIII); h -1 (XXIV); (XXV); (XXVI); Formula (XXIII) indicates that the probability of power outages during the busy farming season is no greater than 5%. Formula (XXIV) indicates that the battery's SOC change rate per hour is no more than 20%. This refers to the change in SOC; The formula (XXV) indicates that the power purchased by the power grid is within a safe range; Formula (XXVI) means: Ensure that the photovoltaic power absorption rate is not less than 45%; Finally, the evaluation is performed using the normalized comprehensive evaluation function, as shown below: (XXVII); in, This represents the overall score; w1, w2, and w3 are all weighting coefficients. This refers to the maximum value of LCC(x) among all candidate solutions; This refers to the minimum value of LCC(x) among all candidate solutions; This refers to the maximum value of PVAR(x) among all candidate solutions; This refers to the minimum value of PVAR(x) among all candidate solutions; This refers to the maximum value of LOLP(x) among all candidate solutions; This refers to the minimum value of LOLP(x) among all candidate solutions; The solution with the highest overall score is recommended as the optimal design.

7. A mobile energy storage collaborative system for agricultural machinery to absorb rooftop photovoltaic power in rural areas, characterized in that, include: The agricultural cycle identification module is configured to: divide the year into busy and slack seasons based on the agricultural operation calendar, and set the functional status of the agricultural machinery battery; during the busy season, the agricultural machinery battery is used only as a power source for agricultural operations and does not participate in the scheduling of the photovoltaic energy storage system; during the slack season, the agricultural machinery battery serves as an energy storage unit for the rooftop photovoltaic system, storing the remaining photovoltaic power and supporting household electricity consumption or electricity price arbitrage. The daily average energy consumption calculation module is configured to calculate the total area of ​​the load curve over the entire cycle, i.e., the total energy consumption, by integration, and then divide by the corresponding number of days to obtain the daily average energy consumption. The photovoltaic power supply capacity calculation module is configured to calculate the daily power generation capacity of the rooftop photovoltaic system, and ensure stable power supply during the busy farming season by using power balance equations and load failure probability models. The economic benefit calculation module is configured to comprehensively calculate the economic benefits of the system, taking into account initial investment, installation costs, operation and maintenance costs, and energy storage revenue, and optimize the cost structure. The charging and discharging strategy optimization module is configured to: optimize the charging and discharging strategy, maximize the absorption of photovoltaic power, and ensure that the agricultural machinery battery effectively stores and releases power to meet the needs of farmers and agricultural machinery; The carbon reduction value correction module is configured to: calculate the theoretical carbon reduction value of photovoltaics and perform probability correction based on actual meteorological data and agricultural machinery operation calendar to eliminate evaluation bias; The multi-objective optimization module is configured to balance economic efficiency, photovoltaic power consumption rate, and reliability through a multi-objective optimization algorithm to generate the optimal design scheme.

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