Intelligent water pump group scheduling system and method based on irrigation demand response

Abstract

The present invention discloses an intelligent water pump group scheduling system and method based on irrigation demand response, which aims to solve the problems of complex scheduling of distributed pump station groups in small watersheds, heavy communication burden and uneven water supply in upstream and downstream. First, the crop water demand, meteorological and soil data are obtained to calculate the irrigation demand. Then, a water pump operation and photovoltaic power generation output model is established. Then, under the premise of maximum photovoltaic power generation utilization, the water pump irrigation power is preliminarily allocated, and surplus or shortage electricity is processed. Finally, the water volume, water level and photovoltaic power generation data are monitored in real time to dynamically adjust the water pump pumping power. Through intelligent decision-making and dynamic adjustment, the present invention not only meets the irrigation demand, but also effectively avoids the problem of insufficient water supply in the downstream caused by excessive pumping in the upstream. At the same time, it optimizes the use of electricity, reduces the cost of introducing external electricity, and realizes the dual optimization of irrigation and electricity use.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to the technical field of gravity open channel water distribution systems, and specifically to an intelligent water pump group scheduling system and method based on irrigation demand response. Background Art

[0002] Pump group control refers to the coordination and optimization of a system composed of multiple pumps to achieve efficient, energy-saving and stable water supply or irrigation. In the field of modern agricultural irrigation, intelligent pump scheduling systems have been widely used. This system integrates data communication, automatic control, Internet of Things and other technologies, combined with a smart agricultural management platform, to remotely monitor and automatically control the pump group in the irrigation area. The system can monitor the operating status and environmental parameters of the pump in real time, automatically adjust the working status of the pump according to irrigation needs, and achieve precise control of irrigation water volume.

[0003] The distribution of pump station clusters refers to the rational arrangement of multiple pump stations in a larger area based on factors such as terrain, water sources, and irrigation needs to form an effective water supply or irrigation network. The rational distribution of pump station clusters can improve the efficiency and reliability of water supply or irrigation and reduce energy consumption and operating costs.

[0004] The intelligent water pump group scheduling system is an automated management system based on advanced technologies such as artificial intelligence, big data, and cloud computing. It can collect and analyze environmental data, crop growth data, water pump operation status data, etc. in the irrigation area in real time, and automatically adjust the working status of the water pump group according to irrigation needs. It can achieve precise control and optimal scheduling of irrigation water volume, improve irrigation efficiency and energy utilization, and provide strong technical support for modern agricultural irrigation.

[0005] Therefore, it is necessary to improve the intelligent water pump group scheduling system and method for irrigation demand response in the prior art to solve the above problems. Summary of the invention

[0006] The present invention overcomes the shortcomings of the prior art and provides an intelligent water pump group scheduling system and method based on irrigation demand response, aiming to solve the problems of complex scheduling of distributed pump station groups in small watersheds in the prior art due to their wide distribution range and large number, and insufficient water supply downstream due to excessive pumping upstream.

[0007] To achieve the above object, the technical solution adopted by the present invention is: an intelligent water pump group scheduling method based on irrigation demand response, comprising:

[0008] S1, obtain the crop water demand, meteorological data and soil moisture of the irrigation area that each water pump is responsible for, and calculate the irrigation demand;

[0009] S2. Establish an operation model of the water pump unit according to the performance parameters of the water pump;

[0010] S3. Establish a photovoltaic power generation output model based on the historical power generation data of the photovoltaic power station, the solar radiation intensity prediction, and the efficiency of the photovoltaic panels;

[0011] S4. Under the premise of maximizing the utilization rate of photovoltaic power generation, combined with the irrigation demand and photovoltaic power generation output model, preliminarily allocate the irrigation power of each water pump, and calculate the power surplus or shortage of each water pump during irrigation;

[0012] S5. In case of power surplus, a storage strategy is formulated and the surplus photovoltaic power is allocated to the water pumps with power shortage. When the surplus photovoltaic power is insufficient to meet the irrigation demand, the best time and amount of external power are intelligently determined, and the cost is optimized in combination with the peak and valley electricity prices.

[0013] S6. Real-time monitoring of upstream and downstream water distribution, water level and water volume data, as well as real-time output of photovoltaic power generation, dynamically adjusts the pumping power of each water pump to ensure that irrigation needs are met while optimizing electricity use.

[0014] In step S1, the step of calculating the irrigation demand includes:

[0015] The reference crop evapotranspiration was calculated using the Penman-Monteith formula.

[0016] Where Rn is the net radiation, G is the soil heat flux, Δ is the slope of the saturated water vapor pressure temperature curve, γ is the psychological constant, u2 is the average wind speed at a height of 2 meters, es is the saturated water vapor pressure, and ea is the actual water vapor pressure;

[0017] Determine the crop coefficient KC based on crop type and growth stage;

[0018] Calculate the crop water requirement ETC = ETO × KC;

[0019] Comprehensive calculation of irrigation demand: Q = (ETC-Peff) × A × Δt, where Q is the irrigation demand, ETC is the crop water requirement, Peff is the effective rainfall, A is the irrigation area, and Δt is the irrigation time interval.

[0020] In step S2, the operation model of the water pump unit is established:

[0021] Obtain the performance parameters of the water pump, including: flow, head, power and efficiency;

[0022] The relationship between flow and head is represented by the performance curve, H = Hmax-k·Q2, where H is the head, k is a constant related to the pump type, and Q is the flow;

[0023] The power of the water pump is Where ρ is the density of the irrigation liquid, g is the acceleration due to gravity, and η is the efficiency of the pump;

[0024] The efficiency of the pump is,

[0025] In step S3, the photovoltaic power generation output model is established as follows:

[0026] Collect power generation data from the historical records of the PV power plant, including: daily power generation, power generation time, and weather conditions;

[0027] Use meteorological departments or professional solar radiation forecasts to obtain forecast data on solar radiation intensity in the future;

[0028] Determine the conversion efficiency of the photovoltaic panels under current conditions based on the model, age and cleanliness of the photovoltaic panels;

[0029] Based on the above data, the cumulative photovoltaic power generation over a period of time is calculated.

[0030] The calculation formula for solar radiation intensity is:

[0031] Among them, Rd is the direct solar radiation intensity, I0 is the solar constant, d is the distance ratio from the earth to the sun, RA is the astronomical radiation, and ZS is the solar altitude angle;

[0032] The efficiency of photovoltaic panels decreases as the temperature increases.

[0033] η′=η0(1-αΔTem) estimates the efficiency change, where η0 is the efficiency under standard test conditions, α is the temperature coefficient, and ΔTem is the temperature rise;

[0034] Photovoltaic power generation PPV is proportional to the solar radiation intensity Rd, photovoltaic panel area AL and photovoltaic panel efficiency η′. The basic formula can be expressed as: PV =R d ·A L ·η′;

[0035] Calculate the cumulative power generation over a period of time, integrate the above formula over time, and assume that the solar radiation intensity remains unchanged over a period of time, then the cumulative power generation EPV can be expressed as: Wherein, t1 and t2 are the start and end time of the calculation period respectively.

[0036] In step S4,

[0037] Calculate a preliminary power allocation plan based on the irrigation demand Qw obtained in step S1 and the photovoltaic power generation PPV obtained in step S3;

[0038] For each pump, calculate the maximum power required Preq, depending on the irrigation demand, the irrigated area and the expected irrigation cycle;

[0039] Compare the maximum power Preq required by each water pump with the available photovoltaic power PPV to determine whether external power supply is needed.

[0040] The calculation of power surplus or deficit during water pump irrigation is:

[0041] When Preq≤PPV, the water pump can be powered directly by the photovoltaic system, and the power surplus Psur=P PV -Preq;

[0042] When Preq>PPV, the pump needs additional power. At this time, the power deficit Pdef=Preq-P PV .

[0043] In step S5, the storage strategy is:

[0044] Assume that the maximum amount of electricity that the battery energy storage system can store is Emax;

[0045] Assume that the amount of electricity that the battery energy storage system can store is Esto. For a certain time period t, the stored energy can be expressed as Esto,t = ∑iPsur,i,t·Δt, where Psur,i,t is the power surplus of pump i in time t, and Δt is the time interval;

[0046] Make sure that the stored charge does not exceed the maximum capacity of the battery Esto,t≤Emax.

[0047] The surplus is distributed as follows:

[0048] The power allocated to the power-deficient pump can be expressed as Ptra,j,t=min(Psur,i,t,Pdef,j,t), where Ptra,j,t is the power transferred from the surplus pump i to the deficit pump j in time t;

[0049] When the stored electricity is insufficient to meet all shortfalls, external electricity needs to be introduced;

[0050] The cost formula for introducing external electricity is Cext,t = (Pdef,t-Ptra,t)·Ppr,t·Δt, where Ppr is the electricity price at time t, Pdef,t is the total power deficit of all pumps at time t, and Ptra,t is the total power transmitted at time t.

[0051] Take advantage of the peak-valley electricity price mechanism to introduce external electricity during periods of lower electricity prices, and avoid using external electricity during peak electricity price periods when it is necessary;

[0052] The objective function of cost optimization is

[0053] Where Qirr,i,t represents the irrigation water volume of pump i in time t; Qi,t is the total irrigation demand at time t; P req,i,t PPV,i,t is the power demand of pump i at time t, PPV,i,t is the photovoltaic power generated by pump i at time t, and Pext,t is the external power introduced at time t.

[0054] Dynamically adjust the pumping power of each water pump based on real-time monitoring data. When the actual irrigation demand in the area where the water pump is located changes, or the real-time power generation of the photovoltaic power station does not match the forecast, immediately adjust the power allocation;

[0055] The power adjustment formula is: Padj,i,t=Preq,i,t-PPV,i,t, where Padj,i,t is the power adjustment of pump i at time t;

[0056] The actual irrigation water volume after power adjustment is Among them, Qirr′,i,t is the irrigation water volume of pump i at time t, η i′ is the efficiency of pump i, ρ is the density of the irrigation liquid, g is the acceleration due to gravity, Hi is the head of the water pump i;

[0057] The total power after power adjustment is Ptot = ∑i(Padj,i,t)+Ptra,t+Pext,t;

[0058] To ensure that irrigation demand is met while optimizing electricity use, the final objective function is

[0059]

[0060] Output the optimal solution.

[0061] The present invention provides an intelligent water pump group scheduling system based on irrigation demand response, comprising:

[0062] Data acquisition module, used to collect data from various sensors and devices;

[0063] Demand calculation module, used to calculate irrigation demand based on crop water demand, meteorological data, soil moisture and other information;

[0064] The water pump operation model module is used to establish an operation model according to the performance parameters of the water pump and calculate the working status of the water pump;

[0065] Photovoltaic power generation model module, used to establish photovoltaic power generation output model;

[0066] An initial power allocation module is used to calculate a preliminary power allocation plan based on irrigation demand and photovoltaic power generation output model;

[0067] A power storage module for storing surplus power in a battery energy storage system;

[0068] A power distribution module for distributing surplus power of the photovoltaic system;

[0069] External power introduction module, used to intelligently decide the best time and amount to introduce external power when photovoltaic power is insufficient;

[0070] Real-time monitoring module, used to monitor upstream and downstream water volume distribution, water level data and real-time output of photovoltaic power generation;

[0071] The adjustment module is used to dynamically adjust the pumping power of each water pump according to the monitoring data.

[0072] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0073] (1) The present invention realizes the coordinated scheduling and optimized management of the pump station group by combining the overall intelligent water pump group scheduling method. It not only takes into account the real-time changes in irrigation demand and power supply, but also solves the optimal scheduling plan through the optimization algorithm, thereby minimizing the cost of introducing external electricity while meeting the irrigation demand. Compared with the existing technology, this method significantly improves the overall efficiency and economic benefits of the pump station group, and provides strong technical support for modern agricultural irrigation.

[0074] (2) The present invention uses step S5 to store and distribute electricity in the case of power surplus, and intelligently decide on the best time and amount of external power to be introduced in the case of power shortage. Combined with the peak-valley electricity price mechanism, the present invention optimizes the cost of electricity use while ensuring irrigation demand, effectively solves the problem of imbalance between electricity supply and demand, realizes the economy of electricity use, and further achieves the effect of reducing operating costs compared with the prior art.

[0075] (3) The present invention monitors the water volume distribution, water level and water volume data of the upstream and downstream in real time through step S6, as well as the real-time output of photovoltaic power generation, and makes dynamic adjustments in combination with the power allocation scheme in step S5. In response to sudden changes, it ensures that irrigation needs are met in a timely manner, improves the resilience of the system, and realizes dynamic optimization of the irrigation process. Compared with the prior art, it further achieves the effect of improving irrigation reliability and flexibility, and avoids the problem of insufficient water supply downstream due to excessive pumping upstream of a small watershed.

[0076] (4) The present invention combines S3 to establish a photovoltaic power generation output model with S4 to preliminarily allocate irrigation power to each water pump, thereby effectively integrating renewable energy into the irrigation system and realizing the preliminary allocation of water pump power under the premise of maximizing photovoltaic power generation utilization. This not only optimizes electricity use and reduces operating costs, but also reduces dependence on traditional power grids, thereby enhancing the sustainability and environmental protection of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 is a flow chart of a preferred embodiment of the present invention;

[0079] Figure 2 It is a schematic diagram of a small watershed distributed pump station group according to a preferred embodiment of the present invention;

[0080] In the figure: 1. Upstream of the small watershed; 2. Irrigation area; 3. Pumping station group; 4. Downstream of the small watershed. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0083] Application Overview:

[0084] As an important part of modern agricultural irrigation and flood control and drainage, small watershed distributed pump station groups have been widely used in remote suburbs or rural areas of towns. Pump station groups not only undertake the task of meeting agricultural and ecological water needs, but also shoulder the responsibility of ensuring the rational allocation of regional water resources. With the growth of irrigation and drainage needs, the number of pump stations has gradually increased, forming a large-scale intelligent pump station group. Small watershed distributed pump station groups are characterized by wide distribution and large scale, and each pump station enjoys equal water access rights.

[0085] Although there are many methods for coordinated dispatching of pumping station groups, they are still insufficient when dealing with smart irrigation and drainage pumping station groups in small watersheds. On the one hand, due to the wide distribution range and large number of pumping stations, the dispatching complexity and data communication volume have increased significantly, increasing the communication burden; on the other hand, when adopting a centralized dispatching strategy, the central control system needs to process a large amount of complex variable information, which makes the calculation complexity increase sharply with the increase in the number of pumping stations; secondly, since each pumping station has equal water withdrawal rights, it is easy to cause excessive pumping upstream and insufficient water supply in the downstream of the small watershed.

[0086] Therefore, this application proposes an intelligent water pump group scheduling method based on irrigation demand response, which aims to improve the communication capability and decision-making level between pumping stations, and to avoid the problem of insufficient water supply downstream caused by excessive pumping upstream of small watersheds while meeting irrigation tasks.

[0087] Exemplary methods:

[0088] like Figure 1 As shown in the flow chart, a smart water pump group scheduling method based on irrigation demand response includes the following steps:

[0089] S1, obtain the crop water demand, meteorological data and soil moisture of the irrigation area that each water pump is responsible for, and calculate the irrigation demand;

[0090] S2. Establish an operation model of the water pump unit according to the performance parameters of the water pump;

[0091] S3. Establish a photovoltaic power generation output model based on the historical power generation data of the photovoltaic power station, the solar radiation intensity prediction, and the efficiency of the photovoltaic panels;

[0092] S4. Under the premise of maximizing the utilization rate of photovoltaic power generation, combined with the irrigation demand and photovoltaic power generation output model, preliminarily allocate the irrigation power of each water pump, and calculate the power surplus or shortage of each water pump during irrigation;

[0093] S5. In case of power surplus, electricity is stored and the surplus photovoltaic power is allocated to the water pumps with power shortage. When the surplus photovoltaic power is not enough to meet the irrigation demand, the best time and amount of external power are intelligently determined, and the cost is optimized in combination with the peak and valley electricity prices.

[0094] S6. Real-time monitoring of upstream and downstream water distribution, water level and water volume data, as well as real-time output of photovoltaic power generation, dynamically adjusts the pumping power of each water pump to ensure that irrigation needs are met while optimizing electricity use.

[0095] like Figure 2 As shown in the schematic diagram of the distributed pump station group in a small watershed, the intelligent water pump group of the present application is arranged along both sides of the river channel in the small watershed to ensure that the irrigation area covered by the pump station is balanced.

[0096] Different crops have different water requirements at different growth stages. The water requirements of crops can be obtained through agricultural research literature, recommended values ​​of local agricultural departments or field measurements.

[0097] Meteorological data include: sunshine hours, daily average temperature, daily maximum temperature, daily minimum temperature, relative humidity, wind speed temperature, humidity, wind speed and rainfall; Meteorological data helps to assess evapotranspiration rate, which is an important factor affecting crop water requirement;

[0098] Soil moisture monitors the moisture content in the soil in real time through a soil moisture sensor;

[0099] In step S1, the step of calculating the irrigation demand includes:

[0100] Reference crop evapotranspiration is calculated using the Penman-Monteith formula, which combines energy balance and aerodynamic theory to calculate evaporation based on standard climate records;

[0101] Where Rn is the net radiation, G is the soil heat flux, Δ is the slope of the saturated water vapor pressure temperature curve, γ is the psychological constant, u2 is the average wind speed at a height of 2 meters, es is the saturated water vapor pressure, and ea is the actual water vapor pressure. In the fields of meteorology and agricultural irrigation, the average wind speed at a height of 2 meters is selected as the standard measurement height. In agricultural and natural ecosystems, the height of crops and vegetation is about 2 meters. In the atmospheric boundary layer, a height of 2 meters is generally considered to be the typical height of the top of the vegetation canopy. The wind speed data at this height can better reflect the energy and material exchange between crops and the atmosphere.

[0102] Determine the crop coefficient KC based on crop type and growth stage;

[0103] Calculate the crop water requirement ETC = ETO × KC;

[0104] Comprehensive calculation of irrigation demand: Qw = (ETC-Peff) × A × Δt, where Qw is the irrigation demand, ETC is the crop water requirement, Peff is the effective rainfall, A is the irrigation area, and Δt is the irrigation time interval;

[0105] In step S2, the operation model of the water pump unit is established:

[0106] Obtain the performance parameters of the water pump, including: flow, head, power and efficiency;

[0107] The relationship between flow rate and head is represented by the performance curve, which is a curve describing the head characteristics of the pump at different flow rates. H = Hmax-k·Q2, where H is the head, k is a constant related to the pump type, and Q is the flow rate.

[0108] The power of the water pump is Among them, ρ is the density of the irrigation liquid, g is the acceleration of gravity, and η is the efficiency of the pump; the power required by the pump to lift the liquid to a certain height is taken into account, while the efficiency of the pump is also taken into account;

[0109] The efficiency of the pump is, The efficiency curve is a part of the pump performance that shows how the pump efficiency changes at different flow rates.

[0110] The models obtained in the above steps are integrated to form a complete pump unit operation model. The output of the model is used to predict the performance of the pump under different working conditions and serves as the basis for the intelligent scheduling method.

[0111] In step S3, the photovoltaic power generation output model is established as follows:

[0112] Collect power generation data from the historical records of the PV power plant, including: daily power generation, power generation time, and weather conditions;

[0113] Use meteorological departments or professional solar radiation forecasts to obtain forecast data on solar radiation intensity in the future;

[0114] Specifically, the calculation formula is:

[0115] Among them, Rd is the direct solar radiation intensity, I0 is the solar constant, d is the distance ratio from the earth to the sun, RA is the astronomical radiation, and ZS is the solar altitude angle;

[0116] Determine the conversion efficiency of the photovoltaic panel under current conditions based on the model, age and cleanliness of the photovoltaic panel; the efficiency of the photovoltaic panel decreases as the temperature increases, and the photovoltaic panel efficiency formula

[0117] η′=η0(1-αΔTem) estimates the efficiency change, where η0 is the efficiency under standard test conditions, α is the temperature coefficient, and ΔTem is the temperature rise;

[0118] Photovoltaic power generation PPV is proportional to the solar radiation intensity Rd, photovoltaic panel area AL and photovoltaic panel efficiency η′. The basic formula can be expressed as: PV =R d ·A L ·η′;

[0119] Calculate the cumulative power generation over a period of time, integrate the above formula over time, and assume that the solar radiation intensity remains unchanged over a period of time, then the cumulative power generation EPV can be expressed as: Wherein, t1 and t2 are the start and end time of the calculation period respectively.

[0120] In step S4,

[0121] Calculate a preliminary power allocation plan based on the irrigation demand Qw obtained in step S1 and the photovoltaic power generation PPV obtained in step S3;

[0122] For each pump, calculate the maximum power required Preq, depending on the irrigation demand, the irrigated area and the expected irrigation cycle;

[0123] By comparing the maximum power Preq required by each pump and the available photovoltaic power PPV, it can be determined whether external power supply is needed;

[0124] When Preq≤PPV, the water pump can be powered directly by the photovoltaic system, and the power surplus Psur=P PV -Preq;

[0125] When Preq>PPV, the pump needs additional power. At this time, the power deficit Pdef=Preq-P PV ;

[0126] For a single water pump i, its required power is defined as Preq,i, which is calculated based on the irrigation demand Qi, irrigation area Ai, and expected irrigation cycle ti:

[0127] Calculate the power surplus or deficit: Psur,i=max(0,P PV ,i-Preq,i), Pdef,i=max(0,Preq,i-PPV,i).

[0128] Through the above calculations, a preliminary power allocation plan can be provided for each pump, and it is possible to identify which pumps may require additional power sources for dynamic adjustment and optimization in subsequent steps.

[0129] In step S5, the storage strategy is:

[0130] Assume that the maximum amount of electricity that the battery energy storage system can store is Emax;

[0131] Assume that the amount of electricity that the battery energy storage system can store is Esto. For a certain time period t, the stored energy can be expressed as Esto,t = ∑iPsur,i,t·Δt, where Psur,i,t is the power surplus of pump i in time t, and Δt is the time interval;

[0132] Make sure that the stored charge does not exceed the maximum capacity of the battery Esto,t≤Emax.

[0133] The surplus is distributed as follows:

[0134] The power allocated to the power-deficient pump can be expressed as Ptra,j,t=min(Psur,i,t,Pdef,j,t), where Ptra,j,t is the power transferred from the surplus pump i to the deficit pump j in time t;

[0135] When the stored electricity is insufficient to meet all shortfalls, external electricity needs to be introduced;

[0136] The cost formula for introducing external electricity is Cext,t = (Pdef,t-Ptra,t)·Ppr,t·Δt, where Ppr is the electricity price at time t, Pdef,t is the total power deficit of all pumps at time t, and Ptra,t is the total power transmitted at time t.

[0137] Take advantage of the peak-valley electricity price mechanism to introduce external electricity during periods of lower electricity prices, and avoid using external electricity during peak electricity price periods when it is necessary;

[0138] The objective function of cost optimization is

[0139] Where Qirr,i,t represents the irrigation water volume of pump i in time t; Qi,t is the total irrigation demand at time t; P req,i,t PPV,i,t is the power demand of pump i at time t, PPV,i,t is the photovoltaic power generated by pump i at time t, and Pext,t is the external power introduced at time t.

[0140] In step S6, the water volume distribution and water level data of the upstream and downstream are monitored in real time to ensure that excessive pumping upstream does not lead to insufficient water supply downstream; the real-time power generation of the photovoltaic power station is monitored so that the scheduling strategy can be adjusted at any time; the water volume and water level data are collected through sensors installed on the river channel, and the data are transmitted to the central control system through the Internet of Things technology; the real-time power generation data is obtained through the monitoring system of the photovoltaic power station and input into the intelligent scheduling system;

[0141] The pumping power of each water pump is dynamically adjusted based on real-time monitoring data to adapt to the current irrigation demand and power supply conditions; when the actual irrigation demand in the area where the water pump is located changes, or the real-time power generation of the photovoltaic power station does not match the forecast, the power distribution is adjusted immediately.

[0142] The power adjustment formula is: Padj,i,t=Preq,i,t-PPV,i,t, where Padj,i,t is the power adjustment of pump i at time t;

[0143] The actual irrigation water volume after power adjustment is Among them, Qirr′,i,t is the irrigation water volume of pump i at time t, η i′ is the efficiency of pump i, ρ is the density of the irrigation liquid, g is the acceleration due to gravity, Hi is the head of the water pump i;

[0144] The total power after power adjustment is Ptot = ∑i(Padj,i,t)+Ptra,t+Pext,t;

[0145] To ensure that irrigation demand is met while optimizing electricity use, the final objective function is

[0146]

[0147] By solving this optimization problem, an optimal scheduling scheme can be found, which can meet the irrigation demand while minimizing the cost of introducing external electricity.

[0148] Example systems:

[0149] An intelligent water pump group scheduling system based on irrigation demand response, comprising:

[0150] Data acquisition module, used to collect data from various sensors and devices;

[0151] Demand calculation module, used to calculate irrigation demand based on crop water demand, meteorological data, soil moisture and other information;

[0152] The water pump operation model module is used to establish an operation model according to the performance parameters of the water pump and calculate the working status of the water pump;

[0153] Photovoltaic power generation model module, used to establish photovoltaic power generation output model;

[0154] An initial power allocation module is used to calculate a preliminary power allocation plan based on irrigation demand and photovoltaic power generation output model;

[0155] A power storage module for storing surplus power in a battery energy storage system;

[0156] A power distribution module for distributing surplus power of the photovoltaic system;

[0157] External power introduction module, used to intelligently decide the best time and amount to introduce external power when photovoltaic power is insufficient;

[0158] Real-time monitoring module, used to monitor upstream and downstream water volume distribution, water level data and real-time output of photovoltaic power generation;

[0159] The adjustment module is used to dynamically adjust the pumping power of each water pump according to the monitoring data.

[0160] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. An intelligent water pump group scheduling method based on irrigation demand response, characterized in that: Includes steps: S1, obtain the crop water demand, meteorological data and soil moisture of the irrigation area that each water pump is responsible for, and calculate the irrigation demand; S2. Establish an operation model of the water pump unit according to the performance parameters of the water pump; S3. Establish a photovoltaic power generation output model based on the historical power generation data of the photovoltaic power station, the solar radiation intensity prediction, and the efficiency of the photovoltaic panels; S4. Under the premise of maximizing the utilization rate of photovoltaic power generation, combined with the irrigation demand and photovoltaic power generation output model, preliminarily allocate the irrigation power of each water pump, and calculate the power surplus or shortage of each water pump during irrigation; S5. In case of power surplus, a storage strategy is formulated and the surplus photovoltaic power is allocated to the water pumps with power shortage. When the surplus photovoltaic power is insufficient to meet the irrigation demand, the best time and amount of external power are intelligently determined, and the cost is optimized in combination with the peak and valley electricity prices. S6. Real-time monitoring of water distribution, water level and water volume data upstream and downstream, as well as real-time output of photovoltaic power generation, dynamically adjusts the pumping power of each water pump to ensure that irrigation needs are met while optimizing electricity use; In step S3, the photovoltaic power generation output model is established as follows: Collect power generation data from the historical records of the PV power plant, including: daily power generation, power generation time, and weather conditions; Use meteorological departments or professional solar radiation forecasts to obtain forecast data on solar radiation intensity in the future; Determine the conversion efficiency of the photovoltaic panels under current conditions based on the model, age and cleanliness of the photovoltaic panels; Calculate the cumulative photovoltaic power generation over a period of time based on the above data; The calculation formula for solar radiation intensity is: Among them, Rd is the direct solar radiation intensity, I0 is the solar constant, d is the distance ratio from the earth to the sun, RA is the astronomical radiation, and ZS is the solar altitude angle; The efficiency of photovoltaic panels decreases as the temperature increases. The efficiency change is estimated by the photovoltaic panel efficiency formula η′=η0(1-αΔTem), where η0 is the efficiency under standard test conditions, α is the temperature coefficient, and ΔTem is the temperature rise; Photovoltaic power generation PPV is proportional to the solar radiation intensity Rd, photovoltaic panel area AL and photovoltaic panel efficiency η′. The basic formula is: PV =R d ·A L ·η′; To calculate the cumulative power generation over a period of time, integrate the above formula over time. Assuming that the solar radiation intensity remains unchanged over a period of time, the cumulative power generation EPV is expressed as: Wherein, t1 and t2 are the start and end time of the calculation period respectively.

2. The method for scheduling an intelligent water pump group based on irrigation demand response according to claim 1, characterized in that: In step S1, the step of calculating the irrigation demand includes: The reference crop evapotranspiration was calculated using the Penman-Monteith formula. Where Rn is the net radiation, G is the soil heat flux, Δ is the slope of the saturated water vapor pressure temperature curve, γ is the psychological constant, u2 is the average wind speed at a height of 2 meters, es is the saturated water vapor pressure, and ea is the actual water vapor pressure; Determine the crop coefficient KC based on crop type and growth stage; Calculate the crop water requirement ETC = ETO × KC; Comprehensive calculation of irrigation demand: Q = (ETC-Peff) × A × Δt, where Q is the irrigation demand, ETC is the crop water requirement, Peff is the effective rainfall, A is the irrigation area, and Δt is the irrigation time interval.

3. The method for scheduling an intelligent water pump group based on irrigation demand response according to claim 1, characterized in that: In step S2, the operation model of the water pump unit is established: Obtain the performance parameters of the water pump, including: flow, head, power and efficiency; The relationship between flow and head is represented by the performance curve, H = Hmax-k·Q2, where H is the head, k is a constant related to the pump type, Q is the flow rate, and Hmax is the maximum head; The power of the water pump is Where ρ is the density of the irrigation liquid, g is the acceleration due to gravity, and η is the efficiency of the pump; The efficiency of the pump is, 4. The method for scheduling an intelligent water pump group based on irrigation demand response according to claim 1, characterized in that: In step S4, Calculate a preliminary power allocation plan based on the irrigation demand Qw obtained in step S1 and the photovoltaic power generation PPV obtained in step S3; For each pump, calculate the maximum power required Preq, depending on the irrigation demand, the irrigated area and the expected irrigation cycle; Compare the maximum power Preq required by each water pump with the available photovoltaic power PPV to determine whether external power supplement is needed.

5. The method for scheduling an intelligent water pump group based on irrigation demand response according to claim 4, characterized in that: The calculation of power surplus or deficit during water pump irrigation is: When Preq≤PPV, the water pump is directly powered by the photovoltaic system, and the power surplus Psur=P PV -Preq; When Preq>PPV, the pump needs additional power. At this time, the power deficit Pdef=Preq-P PV .

6. The method for scheduling an intelligent water pump group based on irrigation demand response according to claim 5, characterized in that: In step S5, the storage strategy is: Assume that the maximum amount of electricity that the battery energy storage system can store is Emax; Assume that the amount of electricity that the battery energy storage system can store is Esto. For a certain time period t, the stored energy is expressed as Esto,t = ∑iPsur,i,t·Δt, where Psur,i,t is the power surplus of pump i in time t, and Δt is the time interval; Ensure that the stored power does not exceed the maximum capacity of the battery Esto,t≤Emax; The surplus is distributed as follows: The power allocated to the power-deficient pump is expressed as Ptra,j,t=min(Psur,i,t,Pdef,j,t), where Ptra,j,t is the power transferred from the surplus pump i to the deficit pump j in time t, and Pdef,j,t is the power deficit of the deficit pump j in time t; When the stored electricity is insufficient to meet all shortfalls, external electricity needs to be introduced; The cost formula for introducing external electricity is Cext,t = (Pdef,t-Ptra,t)·Ppr,t·Δt, where Ppr,t is the electricity price at time t, Pdef,t is the total power deficit of all pumps at time t, and Ptra,t is the total power transmitted at time t; Take advantage of the peak-valley electricity price mechanism to introduce external electricity during periods of lower electricity prices. When external electricity must be used, avoid using it during peak electricity price periods. The objective function of cost optimization is Where Qirr,i,t represents the irrigation water volume of pump i in time t; Qi,t is the total irrigation demand at time t; P req,i,t PPV,i,t is the power demand of pump i at time t, PPV,i,t is the photovoltaic power generated by pump i at time t, and Pext,t is the external power introduced at time t.

7. The method for scheduling an intelligent water pump group based on irrigation demand response according to claim 6, characterized in that: Dynamically adjust the pumping power of each water pump based on real-time monitoring data. When the actual irrigation demand in the area where the water pump is located changes, or the real-time power generation of the photovoltaic power station does not match the forecast, immediately adjust the power allocation; The power adjustment formula is: Padj,i,t=Preq,i,t-PPV,i,t, where Padj,i,t is the power adjustment of pump i at time t; The actual irrigation water volume after power adjustment is Among them, Qirr′,i,t is the irrigation water volume of pump i at time t, η i′ is the efficiency of pump i, ρ is the density of the irrigation liquid, g is the acceleration due to gravity, Hi is the head of the water pump i; The total power after power adjustment is Ptot = ∑i(Padj,i,t)+Ptra,t+Pext,t; To ensure that irrigation demand is met while optimizing electricity use, the final objective function is Output the optimal solution.

8. An intelligent water pump group scheduling system based on irrigation demand response, based on the intelligent water pump group scheduling method based on irrigation demand response according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect data from various sensors and devices; Demand calculation module, used to calculate irrigation demand based on crop water demand, meteorological data, and soil moisture information; The water pump operation model module is used to establish an operation model according to the performance parameters of the water pump and calculate the working status of the water pump; Photovoltaic power generation model module, used to establish photovoltaic power generation output model; An initial power allocation module is used to calculate a preliminary power allocation plan based on irrigation demand and photovoltaic power generation output model; A power storage module for storing surplus power in a battery energy storage system; A power distribution module for distributing surplus power of the photovoltaic system; External power introduction module, used to intelligently decide the best time and amount to introduce external power when photovoltaic power is insufficient; Real-time monitoring module, used to monitor upstream and downstream water volume distribution, water level data and real-time output of photovoltaic power generation; The adjustment module is used to dynamically adjust the pumping power of each water pump according to the monitoring data.

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