A smart farm virtual power plant model with agricultural irrigation load and its optimization scheduling method

Abstract

The present invention discloses a smart farm virtual power plant model and optimization scheduling method for agricultural irrigation loads, belonging to the field of power systems. The model includes: constructing typical scenarios based on the proximity propagation clustering algorithm, constructing an agriculture-power coupling relationship model, and constructing a farm virtual power plant operation cost calculation model. The optimization scheduling method can achieve the operational effects of single-field balancing, multi-field peak shifting, production guarantee, and overall optimization. Utilizing the total cost model for regulating the flexibility resources of the smart farm virtual power plant, a stochastic optimization strategy is run based on the obtained typical scenarios to calculate the optimal regulation and scheduling scheme for the flexibility resources of the smart farm virtual power plant and obtain a day-ahead scheduling strategy. The present invention can effectively improve the regulation level of the smart farm virtual power plant, effectively integrate and optimize distributed energy resources, improve energy utilization efficiency, and promote the development of farm virtual power plants in a more environmentally friendly, efficient, and flexible direction, thereby achieving the sustainable development goals of the power-agriculture coupling system.

Description

Technical Field

[0001] The present invention belongs to the field of power systems, and in particular relates to an intelligent farm virtual power plant model including agricultural irrigation loads and an optimization scheduling method. Background Art

[0002] A virtual power plant (VPP) is an innovative energy management system that leverages advanced information and communication technologies and software systems to aggregate and coordinate the optimization of distributed energy resources, including distributed power sources (such as renewable energy and energy storage systems), controllable loads, and electric vehicles. The core of a VPP lies in aggregation and communication. It connects various distributed energy resources and, through software systems, aggregates and coordinates these resources to form a unique "power plant." A VPP shares the functions of a traditional power plant, enabling precise automatic response. Its unit characteristic curves mimic those of conventional generators. It monitors and forecasts various factors, including energy demand and market prices, in real time, and flexibly dispatches and coordinates distributed energy resources accordingly. For example, it can rapidly increase output when electricity demand increases, while reducing output or storing excess power when there is an oversupply. As a specialized power plant, a VPP participates in electricity markets and grid operations. It can serve as a "positive power plant" to provide peak load regulation to the system, or as a "negative power plant" to increase load absorption and facilitate valley flow control.

[0003] Smart farm virtual power plants typically have a large number of distributed energy resources, such as solar photovoltaic panels, wind turbines, smart irrigation loads, etc.

[0004] The existing scheduling methods for farm virtual power plants still have the following three major difficulties that need to be overcome: 1) The power scheduling plan ignores agricultural production plans, which poses a risk of crop yield reduction. There is a lack of overall consideration of power supply and agricultural production supply, the model of farm output and electricity consumption is unclear, and there is a lack of specific modeling of the relationship chain of irrigation electricity-irrigation water-crop yield; 2) The dynamic change process of farmland soil moisture is not clearly modeled. The irrigation load scheduling model in existing research is too simple and lacks a scientific description of the specific demand for irrigation water. For example, it only considers electric water extraction and ignores the impact of transpiration, making it difficult to effectively guide farmland irrigation and equipment electricity consumption; 3) The production and operation of farm virtual power plants are affected by multiple uncertainties such as precipitation. How to ensure the safe operation and production supply of farm virtual power plants in a multi-uncertainty environment urgently needs further in-depth research. Summary of the Invention

[0005] In response to the above problems, the present invention provides a modeling and optimization scheduling method for a smart farm virtual power plant with agricultural irrigation load, which can effectively improve the regulation level of the smart farm virtual power plant, effectively integrate and optimize distributed energy resources, improve energy utilization efficiency, reduce energy waste, and at the same time alleviate the risks brought by the multiple uncertainties in the agricultural production process to agricultural production supply and virtual power plant operation.

[0006] The technical solution adopted by the present invention is as follows: a method for constructing a smart farm virtual power plant model including agricultural irrigation load, comprising the following steps:

[0007] S1: Construct a typical scenario based on the proximity propagation clustering algorithm: collect weather and electricity price forecast information for the smart farm virtual power plant, and use the clustering algorithm to obtain a typical reference scenario for the virtual power plant's day-ahead scheduling, including wind power equipment output information, photovoltaic equipment output information, market electricity price fluctuation information, and precipitation fluctuation information;

[0008] S2: Constructing an agriculture-electricity coupling relationship model: including constructing a crop water demand model, a soil moisture change model, a crop yield model, and an irrigation water-to-electricity conversion model.

[0009] The crop water requirement model includes a crop coefficient, a crop actual water requirement parameter, a reference crop water requirement parameter, a saturated water vapor pressure curve slope parameter, a surface net radiation parameter, a soil heat flux parameter, a psychrometric constant, a daily average temperature parameter, a wind speed parameter at 2 meters above the surface, a saturated water vapor pressure parameter, and an actual water vapor pressure parameter;

[0010] The soil moisture change model includes soil moisture content parameters, effective rainfall parameters, shallow groundwater daily recharge parameters, irrigation parameters, and soil moisture transpiration parameters.

[0011] The crop yield model includes crop evaporation and transpiration parameters, soil moisture correction coefficient, average soil moisture content parameter in the crop root active layer, soil wilting coefficient, and crop water production function.

[0012] The irrigation water-to-electricity conversion model includes a parameter of farmland irrigation electricity consumption multiplied by a water-to-electricity conversion coefficient;

[0013] S3: Constructing a farm virtual power plant operation cost calculation model to calculate the total cost of the farm virtual power plant's electricity consumption throughout the day and potential production reduction. The farm virtual power plant operation cost calculation model includes constructing a virtual power plant flexibility resource model and a virtual power plant agriculture-electricity operation cost model;

[0014] The virtual power plant flexibility resource model includes a wind and solar power model, an energy storage device model, a reactive power compensation SVG device model, and an irrigation load model. It models the operating principles of all flexibility resources within the virtual power plant, builds the physical basis of the virtual power plant control strategy, and uses power balance constraints to ensure the safety of virtual power plant operation.

[0015] The virtual power plant agriculture-electricity operation cost model calculates the virtual power plant electricity cost, agricultural production reduction cost, and virtual power plant electricity sales income, and then comprehensively calculates the total benefit of the virtual power plant operation.

[0016] Furthermore, in step S1, a clustering algorithm is used to obtain a typical scenario for the day-ahead dispatch of the virtual power plant, as shown in formulas (1)-(4).

[0017] Formula (1) is the calculation method of similarity matrix elements;

[0018]

[0019] Where: s(m,n) is the similarity matrix element, the non-diagonal elements are Euclidean distances; m, m', n, n' are the sequence numbers of the points respectively; x m 、x n are the uncertain parameters respectively;

[0020] Formula (2) is the calculation method of the attraction matrix elements;

[0021]

[0022] Where: r(m,n) and a(m,n') are the elements of the attraction matrix and the affiliation matrix respectively;

[0023] Formula (3) is the calculation method of the attribute matrix elements;

[0024]

[0025] Formula (4) attenuation iterative update calculation;

[0026]

[0027] Where λ represents the damping coefficient and s represents the number of iterations.

[0028] Furthermore, in step S2, formulas (5)-(6) are crop water requirement models.

[0029] ET=K C ×ET R (5)

[0030] Where: K C is the crop coefficient, ET is the actual water requirement of the crop, and ETR is the reference crop water requirement;

[0031]

[0032] Where: Δ is the slope of the saturated water vapor pressure curve; R n is the net radiation of the surface; G is the soil heat flux; γ is the psychrometric constant; T mean is the daily average temperature; u2 is the wind speed at 2 meters above the ground; e s is the saturated water vapor pressure; e a is the actual water vapor pressure;

[0033] Formula (7) is the soil moisture change model,

[0034] W t =W t-1 +P 0,t +K t +M t -ET t (7)

[0035] Where: t is the time; W t is the soil water content at time t; W t-1 is the soil moisture content at the previous moment; P 0,t is the effective rainfall during this period; K t is the shallow groundwater recharge during this period; M t is the irrigation amount during this period; ET t is the soil water evaporation during this period;

[0036] Formulas (8)-(11) are crop yield models.

[0037] ET A =K s ×ET(8)

[0038] Where: ET A is crop evapotranspiration; K s is the soil moisture correction factor;

[0039]

[0040] Where: θ is the average soil moisture content in the crop root active layer during the calculation period; θ f is the upper limit of soil moisture; θ wp is the soil wilting coefficient;

[0041]

[0042] Where: Y a 、Y max ET A,k ETk They represent the actual crop yield, maximum yield, the sum of actual transpiration in production cycle k, and the sum of actual daily crop water requirements in production cycle k; B k is the moisture sensitivity empirical coefficient;

[0043]

[0044] Where: D k,j is the potential yield reduction rate of each farmland per day; j is the day sequence, Y a,k,j 、Y max,k,j ET A,k,j They represent the equivalent output value of farmland per day, the maximum potential output value per day, and the evaporation and transpiration of crops per day respectively;

[0045] Formula (12) is the irrigation water-to-electricity conversion model,

[0046]

[0047] Where: is the irrigation water volume of farmland i during this period; is the electricity consumption of farmland i during this period, T C is the hydropower conversion coefficient.

[0048] Furthermore, in step S3, formula (13) is the wind and solar power model,

[0049]

[0050] Where: are the maximum output power of wind and solar power sources at time t, and the upper limits of wind and solar power output are the predicted output values ​​after uncertainty processing; P t WT 、P t PV are wind power / photovoltaic output values ​​respectively;

[0051] Formulas (14)-(16) are energy storage device models.

[0052]

[0053] Where: is the remaining energy of the battery; Divided into the initial, minimum and maximum remaining energy of the battery; It is the charge / discharge sign of the battery;

[0054]

[0055] Where: P t ESS,in 、P tESS,out are the battery charging / discharging power respectively; They are the upper limits of battery charge / discharge power respectively;

[0056]

[0057] Where: η in ,η out , γ ESS are the charging / discharging efficiency / dissipation rate of the battery respectively; T is the total number of moments; ΔT is the total time period;

[0058] Formula (17) is the reactive power compensation SVG equipment model,

[0059]

[0060] Where: is the maximum reactive compensation power of the SVG device; Contribute to SVG devices;

[0061] Formula (18) is the irrigation load model,

[0062]

[0063] Where: is the maximum power of the irrigation load; P t well is the operating power of the irrigation load;

[0064] Formula (19) is the power balance constraint,

[0065]

[0066] Where: Purchase electricity for virtual power plants; Purchase electricity for the virtual power plant; P fix is a fixed active load; is the reactive power balance of the power grid; Q fix is a fixed reactive load;

[0067] Formula (20) is the soil moisture constraint for agricultural production;

[0068] W i min ≤W i t ≤W i max (20)

[0069] Where: W i t is the soil moisture of farmland i at time t; W imin 、W i max are the lower and upper limits of soil moisture respectively; the agriculture-electricity operation cost model of the virtual power plant is expressed by formula (21):

[0070]

[0071] Where: C is the total cost; The price of electricity purchased from the grid; The price of electricity sold to the grid; Purchase electricity for virtual power plants; Purchase electricity for virtual power plants; D i Y is the potential daily yield reduction rate in this growth stage; m i is the estimated total output value of a single piece of farmland; N is the total number of farmlands.

[0072] Another object of the present invention is to provide a smart farm virtual power plant model system containing agricultural irrigation loads, comprising: a memory, a processor, and a computer program stored on the memory and runnable on the processor, wherein when the computer program is executed by the processor, it implements the above-mentioned method for constructing a smart farm virtual power plant model containing agricultural irrigation loads.

[0073] Another object of the present invention is to provide an optimization scheduling method for a smart farm virtual power plant model system including agricultural irrigation loads, so as to achieve the operation effects of single-field leveling, multi-field peak shifting, production guarantee, and overall optimization. The method is as follows: first, according to the typical scenario generation technology based on the proximity propagation clustering algorithm, a typical scenario for the day-ahead scheduling reference of the virtual power plant is obtained, and the water demand and soil moisture requirements of the crops on that day are calculated according to the parameters in the typical prediction scenario; then, an agricultural-electricity coupling relationship model and a farm virtual power plant operation cost calculation model are constructed, a topological structure for the operation of the virtual power plant is built, and the power balance constraints and agricultural production soil moisture constraints for the operation of the virtual power plant are constructed; finally, the total cost model for the regulation of the flexibility resources of the smart farm virtual power plant is used, and a random optimization strategy is run according to the obtained typical scenario to calculate the optimal regulation and scheduling scheme for the flexibility resources of the smart farm virtual power plant, and obtain the day-ahead scheduling strategy;

[0074] Among them, formula (22) shows the total cost model for regulating the flexibility resources of the smart farm virtual power plant, including the electricity cost of the virtual power plant in a typical scenario, the cost of agricultural production reduction in a typical scenario, and the electricity sales revenue of the virtual power plant in a typical scenario.

[0075]

[0076] Where: U is the number of typical scenes after clustering; D u i The potential daily yield reduction rate for a typical scenario in this growth stage, Ym,u i is the estimated total output value of a single piece of farmland under a typical scenario; p i (u) is the probability of occurrence of a typical scenario; They are the electricity purchased and sold at time t, are the electricity purchase price and electricity sales price at time t respectively.

[0077] Another object of the present invention is to provide an optimization scheduling system for an intelligent farm virtual power plant including agricultural irrigation loads, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program implements the optimization scheduling method described above when executed by the processor.

[0078] The advantages and beneficial effects of the present invention are as follows: 1) It can intelligently dispatch energy supply according to the actual needs of the farm to avoid energy surplus or shortage, thereby saving energy costs; 2) It helps to promote the widespread application of renewable energy, reduce fossil energy consumption and pollutant emissions. In a farm environment, it helps to achieve green development and sustainable management of agriculture; 3) It can ensure the stable operation of the agricultural production process according to the farm's production plan and energy needs. By integrating and optimizing energy resources, the virtual power plant can also provide the farm with a stable power supply to ensure the continuity and efficiency of agricultural production; 4) It can realize real-time perception, response and adjustment of farm energy supply and demand, and improve the flexibility and adaptability of agricultural production; 5) The smart farm virtual power plant can participate in the electricity market and power grid operation as a special power plant. In a farm environment, this means that the farm can use the virtual power plant platform to participate in electricity market transactions with its own distributed energy resources, thereby obtaining additional benefits. By participating in electricity market transactions, farms can also better understand market dynamics and price changes, providing strong support for future energy investment decisions; 6) Provide physical support for the modeling of future optimization planning and operation methods of other virtual power plants containing agricultural irrigation equipment, and promote the development of farm virtual power plants in a more environmentally friendly, efficient and flexible direction, thereby achieving the sustainable development goals of the power-agriculture coupling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 Flowchart of the clustering algorithm;

[0080] Figure 2 Overall flow chart for solving the optimal operation of farm virtual power plants;

[0081] Figure 3 Irrigation plan diagram before optimization;

[0082] Figure 4 Optimized irrigation plan diagram. DETAILED DESCRIPTION

[0083] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0084] Example 1

[0085] A method for constructing a smart farm virtual power plant model including agricultural irrigation loads comprises the following steps:

[0086] S1: Construct a typical scenario based on the proximity propagation clustering algorithm: collect weather and electricity price forecast information of the smart farm virtual power plant, and use the clustering algorithm to obtain a typical scenario for the virtual power plant's day-ahead dispatch reference. The process of the clustering algorithm is as follows: Figure 1 As shown, it includes the output information of wind power equipment, the output information of photovoltaic equipment, the fluctuation information of market electricity price and the fluctuation information of precipitation;

[0087] Among them, the clustering algorithm is used to obtain the typical scenario of virtual power plant day-ahead dispatch reference, as shown in formulas (1)-(4).

[0088] Formula (1) is the calculation method of similarity matrix elements;

[0089]

[0090] Where: s(m,n) is the similarity matrix element, the non-diagonal elements are Euclidean distances; m, m', n, n' are the sequence numbers of the points respectively; x m 、x n are the uncertain parameters respectively;

[0091] Formula (2) is the calculation method of the attraction matrix elements;

[0092]

[0093] Where: r(m,n) and a(m,n') are the elements of the attraction matrix and the affiliation matrix respectively;

[0094] Formula (3) is the calculation method of the attribute matrix elements;

[0095]

[0096] Formula (4) attenuation iterative update calculation;

[0097]

[0098] Where λ represents the damping coefficient and s represents the number of iterations.

[0099] S2: Constructing an agriculture-electricity coupling relationship model: including constructing a crop water demand model, a soil moisture change model, a crop yield model, and an irrigation water-to-electricity conversion model.

[0100] Formulas (5) and (6) are crop water requirement models.

[0101] ET=K C ×ET R (5)

[0102] Where: K C is the crop coefficient, ET is the actual water requirement of the crop, and ET R is the reference crop water requirement;

[0103]

[0104] Where: Δ is the slope of the saturated water vapor pressure curve; R n is the net radiation of the surface; G is the soil heat flux; γ is the psychrometric constant; T mean is the daily average temperature; u2 is the wind speed at 2 meters above the ground; e s is the saturated water vapor pressure; e a is the actual water vapor pressure;

[0105] Formula (7) is the soil moisture change model,

[0106] W t =W t-1 +P 0,t +K t +M t -ET t (7)

[0107] Where: t is the time; W t is the soil water content at time t; W t-1 is the soil moisture content at the previous moment; P 0,t is the effective rainfall during this period; K t is the shallow groundwater recharge during this period; M t is the irrigation amount during this period; ET t is the soil water evaporation during this period;

[0108] Formulas (8)-(11) are crop yield models.

[0109] ET A =K s ×ET(8)

[0110] Where: ET A is crop evapotranspiration; K s is the soil moisture correction factor;

[0111]

[0112] Where: θ is the average soil moisture content in the crop root active layer during the calculation period; θ f is the upper limit of soil moisture; θ wp is the soil wilting coefficient;

[0113]

[0114] Where: Y a 、Y max ET A,k ET k They represent the actual crop yield, maximum yield, the sum of actual transpiration in production cycle k, and the sum of actual daily crop water requirements in production cycle k; B k is the moisture sensitivity empirical coefficient;

[0115]

[0116] Where: D k,j is the potential yield reduction rate of each farmland per day; j is the day sequence, Y a,k,j 、Y max,k,j ET A,k,j They represent the equivalent output value of farmland per day, the maximum potential output value per day, and the evaporation and transpiration of crops per day respectively;

[0117] Formula (12) is the irrigation water-to-electricity conversion model,

[0118]

[0119] Where: is the irrigation water volume of farmland i during this period; is the electricity consumption of farmland i during this period, T C is the hydropower conversion coefficient.

[0120] S3: Constructing a farm virtual power plant operation cost calculation model to calculate the total cost of the farm virtual power plant's electricity consumption throughout the day and potential production reduction. The farm virtual power plant operation cost calculation model includes constructing a virtual power plant flexibility resource model and a virtual power plant agriculture-electricity operation cost model;

[0121] The virtual power plant flexibility resource model includes a wind and solar power model, an energy storage device model, a reactive power compensation SVG device model, and an irrigation load model. It models the operating principles of all flexibility resources within the virtual power plant, builds the physical basis of the virtual power plant control strategy, and uses power balance constraints to ensure the safety of virtual power plant operation.

[0122] The virtual power plant agriculture-electricity operation cost model calculates the virtual power plant electricity cost, agricultural production reduction cost, and virtual power plant electricity sales revenue, and then comprehensively calculates the total benefit of the virtual power plant operation. Formula (13) is the wind and solar power model,

[0123]

[0124] Where: are the maximum output power of wind and solar power sources at time t, and the upper limits of wind and solar power output are the predicted output values ​​after uncertainty processing; P t WT 、P t PV are wind power / photovoltaic output values ​​respectively;

[0125] Formulas (14)-(16) are energy storage device models.

[0126]

[0127] Where: is the remaining energy of the battery; Divided into the initial, minimum and maximum remaining energy of the battery; It is the charge / discharge sign of the battery;

[0128]

[0129] Where: P t ESS,in 、P t ESS,out are the battery charging / discharging power respectively; They are the upper limits of battery charge / discharge power respectively;

[0130]

[0131] Where: η in ,η out , γ ESS are the charging / discharging efficiency / dissipation rate of the battery respectively; T is the total number of moments; ΔT is the total time period;

[0132] Formula (17) is the reactive power compensation SVG equipment model,

[0133]

[0134] Where: is the maximum reactive compensation power of the SVG equipment; Q t SVG Contribute to SVG devices;

[0135] Formula (18) is the irrigation load model,

[0136]

[0137] Where: is the maximum power of the irrigation load; P t well is the operating power of the irrigation load;

[0138] Formula (19) is the power balance constraint,

[0139]

[0140] Where: Purchase electricity for virtual power plants; Purchase electricity for the virtual power plant; P fix is a fixed active load; is the reactive power balance of the power grid; Q fix is a fixed reactive load;

[0141] Formula (20) is the soil moisture constraint for agricultural production;

[0142] W i min ≤W i t ≤W i max (20)

[0143] Where: W i t is the soil moisture of farmland i at time t; W i min 、W i max are the lower and upper limits of soil moisture, respectively;

[0144] The agriculture-electricity operating cost model of the virtual power plant is expressed by formula (21):

[0145]

[0146] Where: C is the total cost; The price of electricity purchased from the grid; The price of electricity sold to the grid; Purchase electricity for virtual power plants; Purchase electricity for virtual power plants; D i Y is the potential daily yield reduction rate in this growth stage; m i is the estimated total output value of a single piece of farmland; N is the total number of farmlands.

[0147] In addition, this embodiment provides an optimization scheduling method for the smart farm virtual power plant model with agricultural irrigation load as described above, as follows: first, according to the typical scenario generation technology based on the proximity propagation clustering algorithm, the typical scenario of the virtual power plant's day-ahead scheduling reference is obtained, and the water demand and soil moisture requirements of the crops on that day are calculated according to the parameters in the typical prediction scenario; then, the agricultural-electricity coupling relationship model and the farm virtual power plant operation cost calculation model are constructed, the topological structure of the virtual power plant operation is built, and the power balance constraints and agricultural production soil moisture constraints of the virtual power plant operation are constructed; finally, the total cost model of the control of the flexible resources of the smart farm virtual power plant is used to run the random optimization strategy according to the obtained typical scenario, and the overall process of solving the optimization problem is as follows: Figure 2 Calculate the optimal control and scheduling scheme of the flexibility resources of the smart farm virtual power plant and obtain the day-ahead scheduling strategy;

[0148] Among them, formula (22) shows the total cost model for regulating the flexibility resources of the smart farm virtual power plant, including the electricity cost of the virtual power plant in a typical scenario, the cost of agricultural production reduction in a typical scenario, and the electricity sales revenue of the virtual power plant in a typical scenario.

[0149]

[0150] Where: U is the number of typical scenes after clustering; D u i The potential daily yield reduction rate for a typical scenario in this growth stage, Y m,u i is the estimated total output value of a single piece of farmland under a typical scenario; p i (u) is the probability of occurrence of a typical scenario; They are the electricity purchased and sold at time t, are the electricity purchase price and electricity sales price at time t respectively.

[0151] Example 2

[0152] The present invention can realize the modeling and optimal scheduling of the intelligent farm virtual power plant with agricultural irrigation load. The production season operation simulation case is constructed with reference to the actual scene. This case has a total of 9 farmlands, each covering an area of ​​300,000 mu, planted with rice; each farmland is equipped with a 5MW intelligent irrigation well group, and an orderly irrigation plan is formulated in a unified manner; in addition to the intelligent irrigation load, the distribution network is equipped with a 1MW wind turbine, a 1MW photovoltaic cell, and a battery with a maximum capacity of 5MW. The irrigation plan before optimization is Figure 3 The optimized irrigation plan is Figure 4It can be seen from the results that the No. 6-9 well group chooses short, large-scale irrigation and staggers the peaks. Most of them choose to start up during the peak period of new energy or the period of low electricity prices. In addition to the early morning, No. 9 also has a short, large-scale irrigation during the photovoltaic peak period. The well groups at other nodes mostly choose to irrigate multiple times and in small quantities throughout the day. The irrigation well group can implement effective staggered irrigation. They all choose to start up during the peak output of wind turbines, peak output of photovoltaics or periods with low electricity prices, and stagger their respective irrigation peaks. The irrigation plan is distributed throughout the 24 hours of the day, and the concentrated power congestion phenomenon caused by disordered irrigation at individual times is alleviated, which proves the effectiveness of the proposed smart farm virtual power plant modeling and optimization scheduling method.

[0153] A comparison of optimization benefits is shown in Tables 1 and 2. As can be seen, the total electricity cost of the distribution network reached 26,886.13 yuan throughout the day, with severe voltage drops and high operational safety risks. The electricity cost of the ordered irrigation plan was 25,978.05 yuan, 3.38% lower than the centralized, unordered irrigation plan. This irrigation strategy achieves irrigation tasks more economically. Furthermore, the irrigation well clusters successfully mitigated voltage congestion. The irrigation plan was distributed throughout the entire 24 hours, alleviating the concentrated power congestion caused by unordered irrigation at specific times, effectively avoiding low voltage issues caused by unordered power consumption at the irrigation wells. For example, the voltage at the end nodes of the lines where the irrigation well clusters are located was reduced by 14.22% and the maximum voltage drop by 52.83% compared to the unordered irrigation plan. The average voltage drop at end node 18 was reduced by 13.90% and the maximum voltage drop by 50.32%, respectively. Network security constraints were effectively maintained.

[0154] Table 1 Economic analysis of irrigation strategies

[0155]

[0156] Table 2 Safety analysis of irrigation strategies

[0157]

[0158] To enhance the comparative results, the comparison example lowered the requirement for soil water retention and simulated the tillering stage after spring rice irrigation to create a potential yield deficit scenario. The optimization strategy using the proposed smart farm virtual power plant modeling and optimization scheduling method was compared with the irrigation strategies obtained using a deterministic optimization method and a full-scenario optimization method. The results are shown in Table 3.

[0159] Table 3 Comparison of optimization strategy efficiency

[0160]

[0161]

[0162] As shown in the table, ignoring precipitation uncertainty and directly using weather forecasts to formulate irrigation plans quickly yields an optimized distribution network scheduling solution. However, this solution under-reserves irrigation water margins for potential droughts, resulting in nearly half of the yield reduction scenarios. Using a full-scenario optimization approach, optimizing for all anticipated scenarios, can ensure no yield reductions, but the computational speed is extremely low, resulting in poor efficiency. Compared to the deterministic optimization scheduling model, the proposed smart farm virtual power plant modeling and optimization scheduling method significantly reduces the number of agricultural production losses from 47% to just 3%, significantly improving the system's ability to ensure food production and supply. Compared to the full-scenario distribution network optimization scheduling model, the proposed smart farm virtual power plant modeling and optimization scheduling method significantly reduces the computational speed at the expense of a slightly higher yield reduction rate, reaching only 0.4% of the full-scenario optimization rate. The proposed method also reduces electricity costs by approximately 4.2% compared to the full-scenario optimization method. This significantly improves optimization computational efficiency while essentially ensuring the effectiveness of the optimization and ensuring a reliable food supply.

Claims

1. A method for constructing a smart farm virtual power plant model containing agricultural irrigation load, characterized in that: The steps include: S1: Construct a typical scenario based on the proximity propagation clustering algorithm: collect weather and electricity price forecast information for the smart farm virtual power plant, and use the clustering algorithm to obtain a typical reference scenario for the virtual power plant's day-ahead scheduling, including wind power equipment output information, photovoltaic equipment output information, market electricity price fluctuation information, and precipitation fluctuation information; S2: Constructing an agriculture-electricity coupling relationship model: including constructing a crop water demand model, a soil moisture change model, a crop yield model, and an irrigation water-to-electricity conversion model. The crop water requirement model includes a crop coefficient, a crop actual water requirement parameter, a reference crop water requirement parameter, a saturated water vapor pressure curve slope parameter, a surface net radiation parameter, a soil heat flux parameter, a psychrometric constant, a daily average temperature parameter, a wind speed parameter at 2 meters above the surface, a saturated water vapor pressure parameter, and an actual water vapor pressure parameter; The soil moisture change model includes soil moisture content parameters, effective rainfall parameters, shallow groundwater daily recharge parameters, irrigation parameters, and soil moisture transpiration parameters. The crop yield model includes crop evaporation and transpiration parameters, soil moisture correction coefficient, average soil moisture content parameter in the crop root active layer, soil wilting coefficient, and crop water production function. The irrigation water-to-electricity conversion model includes a parameter of farmland irrigation electricity consumption multiplied by a water-to-electricity conversion coefficient; S3: Constructing a farm virtual power plant operation cost calculation model to calculate the total cost of the farm virtual power plant's electricity consumption throughout the day and potential production reduction. The farm virtual power plant operation cost calculation model includes constructing a virtual power plant flexibility resource model and a virtual power plant agriculture-electricity operation cost model; The virtual power plant flexibility resource model includes a wind and solar power model, an energy storage device model, a reactive power compensation SVG device model, and an irrigation load model. It models the operating principles of all flexibility resources within the virtual power plant, builds the physical basis of the virtual power plant control strategy, and uses power balance constraints to ensure the safety of virtual power plant operation. The virtual power plant agriculture-electricity operation cost model calculates the virtual power plant electricity cost, agricultural production reduction cost, and virtual power plant electricity sales income, and then comprehensively calculates the total benefit of the virtual power plant operation.

2. The method for constructing a smart farm virtual power plant model containing agricultural irrigation load according to claim 1 is characterized in that: In step S1, a clustering algorithm is used to obtain a typical scenario for the day-ahead dispatch of a virtual power plant, as shown in formulas (1)-(4). Formula (1) is the method for calculating the similarity matrix elements; ， Where: are similarity matrix elements, and the non-diagonal elements take the Euclidean distance; are the serial numbers of the points respectively; are the uncertain parameters respectively; Formula (2) is the calculation method of the attraction matrix elements; ， Where: and are the elements of attraction matrix and belonging matrix respectively; Formula (3) is the calculation method of the attribute matrix elements; ， Formula (4) attenuation iterative update calculation; ， Where, represents the damping coefficient, Indicates the number of iterations.

3. The method for constructing a smart farm virtual power plant model containing agricultural irrigation load according to claim 2 is characterized in that: In step S2, formulas (5)-(6) are the crop water requirement model. ， Where: is the crop coefficient, is the actual water requirement of the crop, is the reference crop water requirement; ， Where: is the slope of the saturated water vapor pressure curve; is the net radiation of the surface; G is the soil heat flux; is the psychrometer constant; is the daily average temperature; u2 is the wind speed at 2 meters above the ground; es is the saturated water vapor pressure; ea is the actual water vapor pressure; Formula (7) is the soil moisture change model, ， Where: t is the time; is the soil moisture content at time t; is the soil moisture content at the previous moment; is the effective rainfall during this period; is the shallow groundwater recharge during this period; is the irrigation amount for this period; is the soil water evaporation during this period; Formulas (8)-(11) are crop yield models. ， Where: is the crop evapotranspiration; is the soil moisture correction factor; ， Where: is the average soil moisture content in the active layer of crop roots during the calculation period; It is the upper limit of the optimal soil moisture; is the soil wilting coefficient; ， Where: They represent the actual crop yield, maximum yield, the sum of actual transpiration in production cycle k, and the sum of actual crop water requirements per day in production cycle k respectively; is the moisture sensitivity empirical coefficient; ， Where: is the potential yield reduction rate of each farmland per day; j is the day sequence, They represent the equivalent output value of farmland per day, the maximum potential output value per day, and the evaporation and transpiration of crops per day respectively; Formula (12) is the irrigation water-to-electricity conversion model, ， Where: is the irrigation water volume of farmland i during this period; is the electricity consumption for irrigation of farmland No. i during this period, is the hydropower conversion coefficient.

4. The method for constructing a smart farm virtual power plant model containing agricultural irrigation load according to claim 3 is characterized in that: In step S3, formula (13) is the wind and solar power model, ， Where: are the maximum output power of wind and solar power sources at time t, respectively. The upper limits of wind and solar power sources are the predicted output values ​​after uncertainty processing; are wind power / photovoltaic output values ​​respectively; Formulas (14)-(16) are energy storage device models. ， Where: is the remaining energy of the battery; Divided into the initial, minimum and maximum remaining energy of the battery; It is the charge / discharge sign of the battery; ， Where: are the battery charging / discharging power respectively; They are the upper limits of battery charge / discharge power respectively; ， Where: are the charging / discharging efficiency / dissipation rate of the battery respectively; T is the total time; is the sum of the time periods; Formula (17) is the reactive power compensation SVG equipment model, ， Where: is the maximum reactive compensation power of the SVG device; Contribute to SVG devices; Formula (18) is the irrigation load model, ， Where: is the maximum power of the irrigation load; is the operating power of the irrigation load; Formula (19) is the power balance constraint, ， Where: Purchase electricity for virtual power plants; Purchase electricity for virtual power plants; is a fixed active load; is the reactive balance of the power grid; is a fixed reactive load; Formula (20) is the soil moisture constraint for agricultural production; ， Where: is the soil moisture of farmland i at time t; are the lower and upper limits of soil moisture, respectively; The agriculture-electricity operating cost model of the virtual power plant is expressed by formula (21): ， Where: is the total cost; The price of electricity purchased from the grid; The price of electricity sold to the grid; Purchase electricity for virtual power plants; Purchase electricity for virtual power plants; is the potential daily yield reduction rate for this growth stage; is the estimated total output value of a single piece of farmland; N is the total number of farmlands.

5. A smart farm virtual power plant model system with agricultural irrigation load, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the computer program is executed by the processor, a method for constructing a smart farm virtual power plant model containing agricultural irrigation loads as described in any one of claims 1 to 4 is implemented.

6. The method for performing optimized scheduling of a smart farm virtual power plant model system including agricultural irrigation load according to claim 5 is characterized by: First, using a typical scenario generation technique based on the proximity propagation clustering algorithm, a typical scenario for the day-ahead dispatch of a virtual power plant is obtained. The water demand and soil moisture requirements of the crops for that day are calculated based on the parameters in the typical forecast scenario. Then, an agriculture-electricity coupling relationship model and a farm virtual power plant operation cost calculation model are constructed. The topological structure for the virtual power plant operation is established, and the power balance constraints and agricultural production soil moisture constraints for the virtual power plant operation are constructed. Finally, using a total cost model for the regulation of the flexible resources of the smart farm virtual power plant, a stochastic optimization strategy is run based on the obtained typical scenarios to calculate the optimal regulation and dispatch scheme for the flexible resources of the smart farm virtual power plant and obtain the day-ahead dispatch strategy. Among them, formula (22) shows the total cost model for regulating the flexibility resources of the smart farm virtual power plant, including the electricity cost of the virtual power plant in a typical scenario, the cost of agricultural production reduction in a typical scenario, and the electricity sales revenue of the virtual power plant in a typical scenario. （22）， Where: U is the number of typical scenes after clustering; The potential daily production reduction rate for a typical scenario in this growth stage is: The estimated total output value of a single piece of farmland under a typical scenario; is the probability of occurrence of a typical scenario; They are the electricity purchased and sold at time t, are the electricity purchase price and electricity sales price at time t respectively.

7. An optimization scheduling system for a smart farm virtual power plant with agricultural irrigation load, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the optimization scheduling method according to claim 6 when executed by the processor.

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