An agricultural park load response optimization method, system and electronic device

Abstract

The application provides an agricultural park load response optimization method, system and electronic equipment, and belongs to the field of power optimization. The method comprises the following steps: determining the maximum response capacity of interruptible load according to the day-ahead operation data of the interruptible load; determining the maximum response capacity of humidification load, temperature control load and light supplement load based on a flexible load monomer mathematical model and according to environmental parameters, the day-ahead operation data of the humidification load, the day-ahead operation data of the temperature control load, the day-ahead operation data of the light supplement load and the minimum daily total amount of photosynthetically active radiation; dividing the flexible load in the agricultural park into multiple resource aggregates with the maximum response capacity of each resource aggregate as the objective function according to the maximum response capacity of each flexible load; and determining the intra-day operation state of each flexible load according to the intra-day response instruction and the maximum response capacity of each resource aggregate. The application can meet the requirement of the response capacity of the power grid side and improve the utilization rate of energy in the agricultural park.

Description

Technical Field

[0001] This invention relates to the field of power optimization, and in particular to a method, system and electronic equipment for optimizing load response in agricultural parks. Background Technology

[0002] For a long time, rural energy has been relegated to the end of the energy network, characterized by low resource utilization, low energy efficiency, low energy density, weak infrastructure, and limited economic affordability. Rural energy development has been slow, with significant imbalances and inadequacies. In reality, rural areas possess abundant renewable resources such as wind, solar, and agricultural waste, as well as rural spatial resources like modern agricultural parks, fishponds, and wasteland. Promoting a grid-source coordination system centered on electricity is crucial for building a resilient rural power grid. However, in the interaction between the power grid and agricultural parks, the lack of accurate assessment of the park's load response characteristics and research on the coordinated operation mode of agricultural load and the power grid has resulted in decentralized, isolated, and localized rural energy utilization. This also means that the power grid's scheduling strategies for park energy planning and management cannot accurately reflect the real-time load conditions of the parks, leading to low overall energy utilization and large peak-to-valley differences in most parks.

[0003] Currently, solutions for flexible loads participating in grid demand response are generally designed for single aggregation over long time scales in typical scenarios. However, due to the small size and variety of individual loads in agricultural parks, the significant differences in resource endowment and energy consumption characteristics, and the weak complementarity of different energy types, if a global integrated optimization method is directly applied to the park's adjustable resources during the response period, the solution is not only complex but also has a long response time, resulting in low resource utilization. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and electronic equipment for optimizing the load response of agricultural parks, which can meet the requirements of the grid-side response capacity and improve the energy utilization rate of agricultural parks.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for optimizing the load response of an agricultural park includes:

[0007] The system acquires environmental parameters of the agricultural park, daily operating data of each flexible load within the agricultural park, and characteristic parameters of each flexible load. The flexible loads within the agricultural park include supplemental lighting loads, humidification loads, temperature control loads, and interruptible loads.

[0008] The maximum response capacity of the interruptible load is determined based on the day-ahead operating data of the interruptible load.

[0009] Based on the environmental parameters and the characteristic parameters of each flexible load in the agricultural park, a mathematical model for each flexible load is established. The mathematical model for each flexible load is used to characterize the mapping relationship between the power of each flexible load in the agricultural park and the environmental parameters.

[0010] Based on the mathematical model of the flexible load unit, and according to the environmental parameters, the daily operating data of the humidification load and the daily operating data of the temperature control load, the constant parameter values ​​of the humidification load and the constant parameter values ​​of the temperature control load are determined by the least recursive square method.

[0011] Based on the environmental parameters, the constant parameter values ​​of the humidification load, and the mathematical model of the flexible load unit, determine the maximum response capacity of the humidification load;

[0012] Based on the environmental parameters, the constant parameter values ​​of the temperature-controlled load, and the mathematical model of the flexible load unit, determine the maximum response capacity of the temperature-controlled load;

[0013] Based on the mathematical model of the flexible load unit, the environmental parameters, the daytime operating data of the supplemental lighting load, and the minimum daily total photosynthetically active radiation, the maximum response capacity of the supplemental lighting load is determined.

[0014] Based on the maximum response capacity of interruptible loads, supplemental lighting loads, humidification loads, and temperature control loads, a resource aggregation model is established with the objective function of maximizing the response capacity of each resource aggregation body. The resource aggregation model is used to divide the flexible loads in the agricultural park into multiple resource aggregation bodies.

[0015] Based on the intraday response instructions and the maximum response capacity of each resource aggregate, the intraday operating status of each flexible load within the agricultural park is determined.

[0016] To achieve the above objectives, the present invention also provides the following solution:

[0017] An agricultural park load response optimization system includes:

[0018] The data acquisition unit is used to acquire environmental parameters of the agricultural park, daily operating data of each flexible load within the agricultural park, and characteristic parameters of each flexible load; the flexible loads within the agricultural park include supplemental lighting loads, humidification loads, temperature control loads, and interruptible loads;

[0019] An interruptible capacity determination unit, connected to the data acquisition unit, is used to determine the maximum response capacity of the interruptible load based on the day-ahead operating data of the interruptible load.

[0020] The individual model building unit, connected to the data acquisition unit, is used to build a mathematical model of the flexible load individual based on the environmental parameters and the characteristic parameters of each flexible load in the agricultural park; the mathematical model of the flexible load individual is used to characterize the mapping relationship between the power of each flexible load in the agricultural park and the environmental parameters.

[0021] The constant parameter determination unit is connected to the single-unit model establishment unit and is used to determine the constant parameter values ​​of the humidification load and the constant parameter values ​​of the temperature control load based on the mathematical model of the flexible load single unit, according to the environmental parameters, the daily operating data of the humidification load and the daily operating data of the temperature control load, using the least recursive square method.

[0022] A humidification capacity determination unit, connected to the constant parameter determination unit, is used to determine the maximum response capacity of the humidification load based on the environmental parameters, the constant parameter values ​​of the humidification load, and the mathematical model of the flexible load unit.

[0023] A temperature control capacity determination unit, connected to the constant parameter determination unit, is used to determine the maximum response capacity of the temperature control load based on the environmental parameters, the constant parameter values ​​of the temperature control load, and the mathematical model of the flexible load unit.

[0024] The supplementary lighting capacity determination unit, connected to the single-unit model establishment unit, is used to determine the maximum response capacity of the supplementary lighting load based on the mathematical model of the flexible load unit, the environmental parameters, the daytime operating data of the supplementary lighting load, and the minimum value of the daily total photosynthetically active radiation.

[0025] The resource aggregation unit, connected to the interruptible capacity determination unit, the humidification capacity determination unit, the temperature control capacity determination unit, and the supplementary lighting capacity determination unit, is used to establish a resource aggregation model based on the maximum response capacity of the interruptible load, the maximum response capacity of the supplementary lighting load, the maximum response capacity of the humidification load, and the maximum response capacity of the temperature control load, with the maximum response capacity of each resource aggregation body as the objective function; the resource aggregation model is used to divide the flexible loads in the agricultural park into multiple resource aggregation bodies.

[0026] The load response unit, connected to the resource aggregation unit, is used to determine the daily operating status of each flexible load in the agricultural park based on the daily response command and the maximum response capacity of each resource aggregation.

[0027] To achieve the above objectives, the present invention also provides the following solution:

[0028] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to enable the electronic device to perform the above-described agricultural park load response optimization method.

[0029] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0030] This invention analyzes the operational characteristics of various flexible loads within an agricultural park, establishes a mathematical model for each flexible load, and uses environmental parameters of the agricultural park and operational data of each flexible load to identify and solve parameters (constant parameter values ​​for humidification and temperature control loads) and calculate response capabilities (maximum response capacity). This enables a flexible assessment of the demand response capability of flexible loads in the agricultural park, fully exploring the controllable potential of rural distributed flexible load resources and improving the energy utilization rate of the agricultural park. Furthermore, based on the maximum response capacity of each flexible load and considering the correlation between loads, while meeting the daily electricity consumption of the park's loads, the flexible loads are aggregated and optimized according to their response characteristics. The flexible loads are pre-integrated into a certain number of resource aggregates. Upon receiving a grid response signal, the flexible loads during peak electricity consumption periods are transferred or reduced, thus directly calling upon the pre-configured resource aggregates after receiving the grid's daily response command, thereby meeting the grid's response capacity requirements. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart of the agricultural park load response optimization method of the present invention;

[0033] Figure 2 This is a schematic diagram of the modules of the agricultural park load response optimization system of the present invention.

[0034] Symbol explanation:

[0035] Data acquisition unit-1, interruptible capacity determination unit-2, single-unit model establishment unit-3, constant parameter determination unit-4, humidification capacity determination unit-5, temperature control capacity determination unit-6, supplementary lighting capacity determination unit-7, resource aggregation unit-8, load response unit-9. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] The purpose of this invention is to provide a method, system, and electronic equipment for optimizing load response in agricultural parks. Taking the flexible loads in greenhouses of agricultural parks as the research object, the invention conducts an in-depth analysis of the operating characteristics of each flexible load, assesses the flexibility of the flexible loads' ability to participate in demand response, and optimizes and integrates the flexible loads in the park into a resource aggregate with complementary characteristics according to their response characteristics, thereby meeting the grid-side response capacity requirements and alleviating the grid's peak-shaving pressure.

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides a method for optimizing the load response of an agricultural park, including:

[0041] S1: Acquire environmental parameters of the agricultural park, daily operating data of each flexible load within the agricultural park, and characteristic parameters of each flexible load. Flexible loads within the agricultural park are divided into four main categories: supplemental lighting loads, humidification loads, temperature control loads, and interruptible loads. Specifically, these include LED (light-emitting diode) lamps, microwave sulfur lamps, acoustic growth promoters, negative pressure fans, circulating fans, fan coil units, electric insecticidal lamps, plasma nitrogen fixation systems, spatial electric fields, fertigation systems, organic fertilizer distributors, nutrient solution sterilizers, submersible pumps, horizontal sprayers, carbon crystal heating plates, far-infrared heating, biogas digester heat pumps, agricultural electric vehicles, ventilation fans, and monitoring systems.

[0042] In this embodiment, environmental parameters and daily operating data of each flexible load are collected through corresponding sensors and other devices.

[0043] S2: Determine the maximum response capacity of the interruptible load based on the day-ahead operating data of the interruptible load.

[0044] Interruptible loads in agricultural park greenhouses, such as biogas digester heat pumps, submersible pumps, plasma nitrogen fixation systems, integrated water and fertilizer systems, LED lights, and nutrient solution sterilizers, offer advantages such as fast response speed and simple control when responding to demand. In this embodiment, the maximum response capacity of the interruptible load is the load reduction amount. The daily operating data of the interruptible load includes the rated power and operating status of the interruptible load at each time point on the daily date.

[0045] The load reduction amount for the i-th interruptible load is determined using the following formula:

[0046]

[0047] Among them, C ILi,re (t) represents the load reduction amount at time t for the i-th interruptible load, N ILi Let P be the number of interruptible loads of type i within the agricultural park. IL,i,n (t) represents the rated power of the nth interruptible load in the i-th interruptible load at time t, s IL,i,n (t) represents the operating state of the nth interruptible load in the i-th interruptible load at time t, s IL,i,n (t) = 1 indicates that the operation has stopped, s IL,i,n (t) = 0 indicates that the system is in operation.

[0048] S3: Based on the environmental parameters and the characteristic parameters of each flexible load within the agricultural park, establish a mathematical model for each flexible load. This mathematical model characterizes the mapping relationship between the power of each flexible load within the agricultural park and the environmental parameters.

[0049] The principles of supplemental lighting load, humidification load, and temperature control load are complex and greatly affected by the environment inside and outside the greenhouse, making direct assessment of the responsive capacity difficult. Therefore, this invention requires first establishing individual mathematical models, then separating and identifying the time-varying parameters in the individual mathematical models to calculate parameter values, in order to assess the maximum response capacity of the three types of loads. In this embodiment, the individual mathematical models of flexible loads include supplemental lighting load power models, humidification load power models, and temperature control load power models.

[0050] (1) The power model for supplemental lighting loads (such as LED lamps, microwave sulfur lamps, etc.) is as follows:

[0051]

[0052]

[0053] Among them, P light (t) represents the power of the supplementary light at time t, S gh Where φ is the total area of ​​the greenhouse, φ0 is the light flux per unit area, C1 is the first correction factor, C2 is the second correction factor, and η is the total area of ​​the greenhouse. light For the electro-optical conversion efficiency of the supplemental light, I e (t) represents the average illuminance at time t, I set Let λ be the light intensity for the indoor environment, and I be the greenhouse light transmittance. out (t) represents the outdoor light intensity at time t, k light This is the illuminance conversion factor.

[0054] (2) The power model for humidification loads (such as submersible pumps, horizontal sprayers, etc.) is as follows:

[0055] H pen =N pen m penα[e'(T in )-e(T in )]=H in -H crop -H soil -H air ;

[0056] P pen (t)=N pen (t)P' pen ;

[0057] Among them, H pen For water vapor generated by spray humidification, N pen m is the number of spray nozzles. pen Let α be the water spray rate per unit time of a single spray nozzle, and α be the coefficient of influence of indoor water vapor pressure difference on the water evaporation rate, e'(T) in The greenhouse temperature is T. in The saturated vapor pressure at time e(T) in The greenhouse temperature is T. in The actual water vapor pressure at that time, T in For indoor temperature, H in H represents the humidity inside the greenhouse. crop H is the water vapor released during photosynthetic respiration of crops. soil H is water vapor that evaporates from the soil surface. air For water vapor lost during ventilation, P pen P' represents the total power of the humidification load. pen This refers to the power of a single spray nozzle.

[0058]

[0059]

[0060] Among them, h avg x represents the average height of the greenhouse. in Let x' be the indoor water vapor density. in (T in The greenhouse temperature is T. in saturated water vapor density at h in Let M be the relative humidity of the greenhouse, A be the molar mass of water, e0 be the ideal gas constant, and e0 be the saturated water vapor pressure at 0°C.

[0061]

[0062]

[0063] e(T in ) = h in e'(T in );

[0064]

[0065] v h =2500.8-2.367T in ;

[0066] Wherein, k(T) in The greenhouse temperature is T. in Slope of the saturated vapor pressure curve, X n C represents the net radiation received by the crop, ρ represents the greenhouse air density, and C represents the net radiation received by the crop. air r is the specific heat capacity of air. a For the aerodynamic drag of the crop surface, r s For pore resistance, v h Where γ is the latent heat of vaporization, ξ is the energy coefficient of evaporation, and S is the extinction coefficient of the crop canopy. LAI X is the crop leaf area index. rad denoted as , where is the solar radiation intensity and c is the effective light radiation coefficient.

[0067]

[0068] Among them, S soil Where β is the greenhouse soil area, T is the relative moisture content of the ground, and T is the soil surface area. soil This refers to the soil surface temperature.

[0069] H air =H n +H f =G n (x in -x out )+G f (x in -x out );

[0070]

[0071] Among them, H n H represents the amount of water vapor exchanged through natural ventilation. f G represents the water vapor exchange rate during ventilation by the fan. n x represents the natural ventilation rate of the greenhouse. in For indoor water vapor density, x out G represents the outdoor water vapor density. f S is the fan ventilation rate. win C represents the area of ​​the greenhouse windows. o Where is the flow coefficient, g is the acceleration due to gravity, h is the vertical height of the skylight, and C is the flow rate coefficient. n For the comprehensive wind pressure coefficient, W out ω represents the outdoor wind speed, ω represents the opening angle of the greenhouse window, and T represents the outdoor wind speed. out This refers to the outdoor temperature.

[0072] (3) The power model for temperature-controlled loads (such as fan coil units, carbon crystal heating plates, far-infrared heating, etc.) is as follows:

[0073]

[0074] Q rad =S m I out τ r ;

[0075] Q air =ρ o C air (T in -T out (G) n +G f );

[0076]

[0077] Among them, V gh For the greenhouse volume, Q rad Q represents the heat radiated by solar radiation. air In order to exchange heat with the outside environment, Q heat Q represents the heating capacity of the heating equipment. crop S absorbs heat through crop transpiration. m I represents the area of ​​the greenhouse covering layer. out τ is the total outdoor radiative flux density. r ρ is the total light transmittance of the material. o This refers to the outdoor air density.

[0078] Based on the growth characteristics of different crops in the greenhouse, the maximum and minimum indoor temperatures are set. The temperature control equipment performs relative state start-stop electrothermal conversion, and the temperature control load power can be expressed as:

[0079] Q heat (t)=η heat P heat s heat (t);

[0080]

[0081] Among them, Q heat (t) represents the heating amount of the heating equipment at time t, η heat For the energy efficiency ratio of carbon crystal heating plates, P heat For the load power of the carbon crystal heating plate, s heat (t) represents the operating state of the temperature-controlled load at time t, where T in (t) represents the indoor temperature at time t, T in,max Set the maximum indoor temperature, Tin,min Set a minimum indoor temperature, and ε is the data sampling interval.

[0082] S4: Based on the mathematical model of the flexible load unit, and according to the environmental parameters, the daily operating data of the humidification load and the daily operating data of the temperature control load, the constant parameter values ​​of the humidification load and the constant parameter values ​​of the temperature control load are determined by the least recursive square method.

[0083] Furthermore, S4 specifically includes:

[0084] S41: Integrate the humidification load power model over a set period of time to obtain a humidity change balance model within the set period of time.

[0085] Specifically, for the humidification load power model, t p Time to t q Integrating at time t, we obtain the humidity change equilibrium model for that time period as follows:

[0086]

[0087] S42: Based on the sampling interval of the daily operating data of the humidification load and the humidification load power model, separate the constant parameters and time-varying parameters of the humidity change balance model, and construct the identification expression of the time-varying parameters of the humidification load.

[0088] The humidity change balance model has many variables. Based on the calculation relationship between the variables in the humidification load power model, constant parameters and time-varying parameters are separated:

[0089]

[0090]

[0091]

[0092]

[0093] Among them, P f The rated power of the fan, W f (t) represents the ventilation speed of the fan at time t.

[0094] Let the sampling interval of each data point be ε, and discretize it to obtain the identification expression of the time-varying parameters of the humidification load: K1X1(t)=Y H -K2X2-K3X3-K4X4-K5X5-K6X6-K7X7;

[0095] Among them, the parameters to be determined (constant parameters) are:

[0096]

[0097] The variable (time-varying parameter) is:

[0098]

[0099] Due to indoor water vapor density x in Greenhouse relative humidity h in Outdoor water vapor density x out Indoor temperature T in Outdoor temperature T out Soil temperature T soil outdoor wind speed W out All data can be obtained from temperature and humidity sensors inside and outside the greenhouse and basic meteorological data. The number of sprayers N is also available. pen and fan operating status s f (t) can be obtained through equipment operation data, so X1-X7 are all easily obtainable sample data, and K1-K7 are parameters to be identified.

[0100] S43: Determine the time-varying parameter value of the humidification load based on the environmental parameters and the daily operating data of the humidification load.

[0101] Specifically, Y is calculated for each time period based on the daily operating data of environmental parameters and humidification load. H X1, X2, X3, X4, X5, X6, X7, and the least recursive squares method is used for model parameter identification:

[0102] make

[0103] θ(k)=[K1(k) K2(k) K3(k) K4(k) K5(k) K6(k) K7(k)] T ;

[0104]

[0105]

[0106] Where θ(k) is the parameter identification vector; y(k) represents the output vector; This represents the input vector.

[0107] make The model parameter identification results can be obtained by iteratively updating the θ value according to the following formula:

[0108]

[0109] H(k) and P(k) in the formula are used to iteratively update the θ value to obtain the parameter identification values ​​of X1, X2, X3, X4, X5, X6, and X7.

[0110] Where θ(k) is the new parameter identification vector; θ(k-1) is the parameter identification vector of the previous iteration; H(k) is the gain vector; λ' is the forgetting factor, which is usually between 0.9 and 1.0; and P(k) is the intermediate vector of the correction amount.

[0111] S44: Based on the time-varying parameter values ​​of the humidification load and the identification expression of the time-varying parameter values ​​of the humidification load, the constant parameter values ​​of the humidification load are determined by the least recursive square method.

[0112] S45: Integrate the temperature-controlled load power model over a set period of time to obtain a temperature change balance model within the set period of time.

[0113] Specifically, the power model of the temperature-controlled load is subjected to t p Time to t q Integrating at time points yields the temperature change equilibrium model within the greenhouse during that time period:

[0114]

[0115] S46: Based on the sampling interval of the day-ahead operating data of the temperature-controlled load and the power model of the temperature-controlled load, separate the constant parameters and time-varying parameters of the temperature change balance model, and construct the identification expression of the time-varying parameters of the temperature-controlled load.

[0116] Let the sampling interval of each data point be ε, and discretize it to obtain the identification expression for the time-varying parameters of the temperature-controlled load:

[0117] Y T =M1Z1+M2Z2+M3Z3+M4Z4+M5Z5-M6Z6-M7Z7;

[0118] Among them, the parameters to be determined (constant parameters) are:

[0119]

[0120] The variable (time-varying parameter) is:

[0121]

[0122] Similarly, M1-M7 are the temperature control load parameters to be identified.

[0123] S47: Determine the time-varying parameter value of the temperature control load based on the environmental parameters and the daily operating data of the temperature control load.

[0124] Specifically, Y is calculated for each time period based on the daily operating data of environmental parameters and temperature control load. TZ1, Z2, Z3, Z4, Z5, Z6, Z7, and the least recursive squares method is used to identify the model parameters. The specific process is the same as the process of identifying the time-varying parameter values ​​of the humidification load in S43, and will not be repeated here.

[0125] S48: Based on the time-varying parameter values ​​of the temperature-controlled load and the identification expression of the time-varying parameter of the temperature-controlled load, the constant parameter values ​​of the temperature-controlled load are determined by the least recursive squares method.

[0126] S5: Determine the maximum response capacity of the humidification load based on the environmental parameters, the constant parameter values ​​of the humidification load, and the mathematical model of the flexible load unit.

[0127] Furthermore, S5 specifically includes:

[0128] S51: Based on the environmental parameters and the constant parameter values ​​of the humidification load, determine the maximum continuous on time of the humidification load, the maximum continuous off time of the humidification load, and the humidification load control cycle.

[0129] When the sprinkler system is activated during the demand response period, it responds quickly by appropriately reducing the humidity range without affecting crop growth conditions. Assume the indoor and outdoor temperatures and outdoor water vapor density x during the response period. out Given that soil temperature and outdoor wind speed remain constant, the spraying equipment is activated for τ hours within one cycle. pen,on The closing time is τ pen,off The indoor water vapor density changes from x under humidification load conditions when the unit is on and off. in1 To x in2 The duration can be calculated using the following formula:

[0130]

[0131]

[0132] Indoor water vapor density is determined by x in1 To x in2 For ease of calculation, it is assumed that the indoor and outdoor temperatures, outdoor water vapor density, soil temperature, and outdoor wind speed remain constant during this period. Therefore, the numerator and denominator in In() differ only in the last term, with the numerator being x. in2 At time x, the denominator is x in1 time.

[0133] Based on the above formula, the upper and lower limits of indoor humidity are set, and thus the maximum continuous operating time τ of the humidifier load is obtained. pen,on (x min ,x max ) and maximum continuous shutdown time τ pen,off (x min ,x max) and humidification load control cycle τ pen,c :

[0134] τ pen,c =τ pen,on +τ pen,off .

[0135] S52: Determine the maximum response capacity of the humidification load based on the maximum continuous shutdown time of the humidification load, the humidification load control cycle, and the humidification load power model.

[0136]

[0137] Among them, P pen N' represents the electrical power consumption of the humidification load. pen The number of polymers is determined by the humidification load.

[0138] S6: Determine the maximum response capacity of the temperature-controlled load based on the environmental parameters, the constant parameter values ​​of the temperature-controlled load, and the mathematical model of the flexible load unit.

[0139] Specifically, S6 includes: determining the maximum continuous on-time, maximum continuous off-time, and control cycle of the temperature-controlled load based on the environmental parameters and the constant parameter values ​​of the temperature-controlled load; and determining the maximum response capacity of the temperature-controlled load based on the maximum continuous off-time, control cycle, and power model of the temperature-controlled load.

[0140] When the greenhouse receives a demand response control signal, it adjusts the set range of the air conditioning temperature to obtain the response potential of the temperature control load. Assuming that the outdoor temperature, outdoor radiant flux, indoor and outdoor water vapor density, and outdoor wind speed remain constant during the response period, the on-time of the temperature control load in one cycle is τ. heat,on The closing time is τ heat,off The time it takes for the room temperature to change from T1 to T2 under the conditions of the temperature-controlled load being on and off can be calculated by the following formula:

[0141]

[0142]

[0143] Based on the above formula, by setting the upper and lower limits of the indoor temperature, the maximum continuous operating time τ of the temperature control load can be obtained. heat,on (T min ,T max ) and maximum continuous shutdown time τ heat,off (T min ,T max ), and the load control cycle τ heat,c :

[0144] τ heat,c =τheat,on +τ heat,off ;

[0145] The maximum response capacity of temperature-controlled loads participating in demand response during a given period is:

[0146]

[0147] Among them, P heat N represents the power consumption of the temperature-controlled load; heat The number of temperature-controlled loads.

[0148] S7: Determine the maximum response capacity of the supplemental lighting load based on the mathematical model of the flexible load unit, the environmental parameters, the daily operating data of the supplemental lighting load, and the minimum daily total photosynthetically active radiation.

[0149] Specifically, the daily operational data for supplemental lighting loads includes the operational status and minimum operating duration at each time point in the day. There are multiple supplemental lighting loads within the agricultural park. To determine whether a supplemental lighting load has dispatchable potential at a given time, it can be assumed that the load begins responding to dispatch at that time, continues supplemental lighting after the dispatch ends, and if the Daily Light Integral (DLI) after demand response meets the minimum DLI threshold, then the supplemental lighting load at that time is considered to have dispatchable potential. S7 specifically includes:

[0150] S71: For any supplementary lighting load, determine the power of the supplementary lighting load at each time before the day based on the supplementary lighting load power model of the supplementary lighting load.

[0151] S72: Based on the power at each time before the supplemental lighting load, the environmental parameters, the minimum operating duration of the supplemental lighting load, and the operating status of the supplemental lighting load at each time before the supplemental lighting load, determine the total daily photosynthetically active radiation of the supplemental lighting load. Specifically, the total daily photosynthetically active radiation of the supplemental lighting load is determined using the following formula:

[0152]

[0153] Among them, DLI DR The total daily photosynthetically active radiation (DLI) after demand response, t min For the minimum operating time of the fill light, s light (t) represents the operating status of the supplementary light at time t, and T represents the number of time periods in a day.

[0154] S73: Determine the response capacity of the supplementary lighting load based on the power of the supplementary lighting load, the total daily photosynthetically active radiation of the supplementary lighting load, and the minimum daily total photosynthetically active radiation.

[0155] Assuming all crops in the greenhouse are high-light crops, and the DLI boundary value thresholds are set to 20 and 30 mol / (m²), respectively. 2 ·d), then the response capacity of the j-th supplementary lighting device. for:

[0156]

[0157] S74: Determine the maximum response capacity of the supplementary lighting load based on the response capacity of each supplementary lighting load:

[0158]

[0159] S8: Based on the maximum response capacity of interruptible loads, supplemental lighting loads, humidification loads, and temperature control loads, a resource aggregation model is established with the objective function of maximizing the response capacity of each resource aggregation. This resource aggregation model is used to divide the flexible loads within the agricultural park into multiple resource aggregations. These resource aggregations are ordered from largest to smallest according to their maximum response capacity.

[0160] The resource aggregation model refers to using a one-month timescale for load aggregation, taking the maximization of the aggregate response capacity as the objective function, and pre-integrating the park's load into a certain number of resource aggregates based on the response capacity assessment results of flexible loads. After solving, it was found that the greenhouse contains nearly 20 types of loads, including supplemental lighting loads, humidification loads, temperature control loads, and other interruptible loads. These individual loads are small in quantity, and their resource endowments and energy consumption characteristics vary significantly. Directly optimizing the park's adjustable resources globally during the response period would be not only complex but also time-consuming. This invention integrates these nearly 20 types of loads into a certain number (3 or 4) of resource aggregates. By setting weighting factors, flexible loads with larger response capacities are prioritized for aggregation. During daily operation, the pre-configured resource aggregates are directly called according to priority, thereby meeting the grid-side response capacity.

[0161] In this embodiment, the objective function of the resource aggregation model is:

[0162]

[0163] Among them, F DR Let L be the objective function value, D be the total number of resource aggregates, and ω be the number of load categories. l C is the weighting factor for resource aggregate l. d,re The maximum response capacity of the d-th type of load (including supplemental lighting load C) light,re Humidification load C pen,re Temperature control load C heat,re Other interruptible loads C ILi,re ), λ d,lTo determine whether the d-th type of load belongs to resource aggregate l, if the d-th type of load belongs to resource aggregate l, then λ d,l =1, otherwise λ d,l =0.

[0164] The constraints of the resource aggregation model include:

[0165] (1) Load aggregation frequency constraint, that is, each type of load can only be assigned to one resource aggregate:

[0166]

[0167] (2) Load coupling constraint: For load devices d1 and d2 with a coupling relationship, such as submersible pumps and translational sprayers, when the submersible pump stops running, the translational sprayer will not be able to operate independently due to lack of water supply. Therefore, during aggregation, they tend to be arranged in the same aggregate:

[0168]

[0169] (3) Response capacity constraint: To prevent excessive differences in response capacity between aggregates, the aggregation response capacity of each resource aggregate must be greater than the minimum aggregation capacity C during aggregation. minre :

[0170]

[0171] (4) Response rate constraint: The response rate and recovery rate of each polymer after polymerization should be less than the maximum response rate and maximum recovery rate.

[0172]

[0173]

[0174] Where, r l,up r is the response rate of resource aggregate l. d,up Let d be the response rate of the load type. For the maximum response rate, r l,down r is the recovery rate of resource aggregate l. d,down Let d be the recovery rate of the load type d. This represents the maximum recovery rate.

[0175] (5) Due to greenhouse environmental constraints, the temperature, humidity, and light in each greenhouse must meet the minimum requirements for crop growth during polymer response:

[0176] T min ≤T in ≤T max ;

[0177] xmin ≤x in ≤x max ;

[0178] DLI min ≤DLI≤DLI max .

[0179] T min The lowest temperature in the greenhouse, T max x represents the highest temperature in the greenhouse. min x represents the lowest indoor water vapor density. max The indoor water vapor density is the highest value, and DLI is the total daily photosynthetically active radiation. min DLI represents the lowest daily total photosynthetically active radiation. max This represents the highest daily total of photosynthetically active radiation.

[0180] S9: Determine the intraday operating status of each flexible load within the agricultural park based on the intraday response command and the maximum response capacity of each resource aggregate. The intraday response command is the total intraday demand response capacity.

[0181] This invention addresses demand response on a per-aggregate basis during intraday scheduling, thereby satisfying the grid-side response capacity. Specifically, for resource aggregators 1 to a, it determines whether the sum of the maximum response capacities of these aggregators is greater than or equal to the total intraday demand response capacity. If so, it controls the operation of each humidification load, each temperature-controlled load, and each interruptible load within these aggregators to cease operation. The intraday operating status of each humidification load is controlled based on the humidification load control cycle, and the intraday operating status of each temperature-controlled load is controlled based on the temperature-controlled load control cycle. It also controls the operation of supplementary lighting loads within these aggregators whose response capacity is greater than 0 to cease operation. If not, it determines whether the sum of the maximum response capacities of resource aggregators 1 to a+1 is greater than or equal to the total intraday demand response capacity, until all resource aggregators participate in scheduling control. At this point, the actual intraday response capacity is the sum of the maximum response capacities of resource aggregators 1 to A. Here, a = 1, 2, ..., A, where A is the number of resource aggregators.

[0182] This invention focuses on flexible loads in greenhouses within agricultural parks. By analyzing the operational characteristics of various agricultural loads, it establishes individual load power models and utilizes collected environmental parameters and operational data to identify and solve model parameters and calculate response capability indicators. This enables a flexible assessment of the demand response capability of flexible loads in the park, fully exploring the controllable potential of rural distributed flexible load resources. It effectively promotes the coordinated development of rural distributed resources and the power grid, not only improving the efficiency of rural energy utilization but also providing technical support for the construction of an energy internet in rural areas, and facilitating the construction of resilient rural power grids in rural areas.

[0183] Based on the assessment results of agricultural load response capacity, this invention considers the correlation between loads. While meeting the production electricity needs of the park during daily operation, it performs aggregate optimization analysis on the park load and integrates the park load into a certain number of resource aggregates in advance according to the response characteristics. After receiving the power grid response signal, the flexible load during the peak electricity consumption period of the park is transferred or reduced, thereby meeting the power grid response capacity requirements, relieving the pressure of power grid peak regulation, and improving the flexibility of agricultural park operation.

[0184] Example 2

[0185] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, an agricultural park load response optimization system is provided below.

[0186] like Figure 2 As shown, the agricultural park load response optimization system provided in this embodiment includes: a data acquisition unit 1, an interruptible capacity determination unit 2, a single-unit model establishment unit 3, a constant parameter determination unit 4, a humidification capacity determination unit 5, a temperature control capacity determination unit 6, a supplementary lighting capacity determination unit 7, a resource aggregation unit 8, and a load response unit 9.

[0187] The data acquisition unit 1 is used to acquire environmental parameters of the agricultural park, daily operating data of each flexible load within the agricultural park, and characteristic parameters of each flexible load. The flexible loads within the agricultural park include supplemental lighting loads, humidification loads, temperature control loads, and interruptible loads.

[0188] Interruptible capacity determination unit 2 is connected to the data acquisition unit 1. Interruptible capacity determination unit 2 is used to determine the maximum response capacity of the interruptible load based on the day-ahead operating data of the interruptible load.

[0189] The individual unit model building unit 3 is connected to the data acquisition unit 1. The individual unit model building unit 3 is used to build individual mathematical models of flexible loads based on the environmental parameters and the characteristic parameters of each flexible load within the agricultural park. The individual mathematical models of flexible loads are used to characterize the mapping relationship between the power of each flexible load within the agricultural park and the environmental parameters.

[0190] The constant parameter determination unit 4 is connected to the single-unit model establishment unit 3. The constant parameter determination unit 4 is used to determine the constant parameter values ​​of the humidification load and the constant parameter values ​​of the temperature control load based on the mathematical model of the flexible load single unit, according to the environmental parameters, the daily operating data of the humidification load and the daily operating data of the temperature control load, using the least recursive square method.

[0191] The humidification capacity determination unit 5 is connected to the constant parameter determination unit 4. The humidification capacity determination unit 5 is used to determine the maximum response capacity of the humidification load based on the environmental parameters, the constant parameter value of the humidification load, and the mathematical model of the flexible load unit.

[0192] The temperature control capacity determination unit 6 is connected to the constant parameter determination unit 4. The temperature control capacity determination unit 6 is used to determine the maximum response capacity of the temperature control load based on the environmental parameters, the constant parameter values ​​of the temperature control load, and the mathematical model of the flexible load unit.

[0193] The supplementary lighting capacity determination unit 7 is connected to the single-unit model establishment unit 3. The supplementary lighting capacity determination unit 7 is used to determine the maximum response capacity of the supplementary lighting load based on the mathematical model of the flexible load unit, the environmental parameters, the daily operating data of the supplementary lighting load, and the minimum value of the daily total photosynthetically active radiation.

[0194] Resource aggregation unit 8 is connected to interruptible capacity determination unit 2, humidification capacity determination unit 5, temperature control capacity determination unit 6, and supplemental lighting capacity determination unit 7, respectively. Resource aggregation unit 8 is used to establish a resource aggregation model based on the maximum response capacity of the interruptible load, the maximum response capacity of the supplemental lighting load, the maximum response capacity of the humidification load, and the maximum response capacity of the temperature control load, with the objective function being the maximum response capacity of each resource aggregate. The resource aggregation model is used to divide the flexible loads within the agricultural park into multiple resource aggregates.

[0195] The load response unit 9 is connected to the resource aggregation unit 8. The load response unit 9 is used to determine the daily operating status of each flexible load in the agricultural park based on the daily response command and the maximum response capacity of each resource aggregation.

[0196] Compared with the prior art, the agricultural park load response optimization system provided in this embodiment has the same beneficial effects as the agricultural park load response optimization method provided in Embodiment 1, and will not be repeated here.

[0197] Example 3

[0198] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute the agricultural park load response optimization method of Embodiment 1.

[0199] Alternatively, the aforementioned electronic device may be a server.

[0200] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the agricultural park load response optimization method of Embodiment 1.

[0201] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0202] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing the load response of an agricultural park, characterized in that, The agricultural park load response optimization method includes: The system acquires environmental parameters of the agricultural park, daily operating data of each flexible load within the agricultural park, and characteristic parameters of each flexible load. The flexible loads within the agricultural park include supplemental lighting loads, humidification loads, temperature control loads, and interruptible loads. The maximum response capacity of the interruptible load is determined based on the day-ahead operating data of the interruptible load. Based on the environmental parameters and the characteristic parameters of each flexible load in the agricultural park, a mathematical model for each flexible load is established. The mathematical model for each flexible load is used to characterize the mapping relationship between the power of each flexible load in the agricultural park and the environmental parameters. Based on the mathematical model of the flexible load unit, and according to the environmental parameters, the daily operating data of the humidification load and the daily operating data of the temperature control load, the constant parameter values ​​of the humidification load and the constant parameter values ​​of the temperature control load are determined by the least recursive square method. Based on the environmental parameters, the constant parameter values ​​of the humidification load, and the mathematical model of the flexible load unit, determine the maximum response capacity of the humidification load; Based on the environmental parameters, the constant parameter values ​​of the temperature-controlled load, and the mathematical model of the flexible load unit, determine the maximum response capacity of the temperature-controlled load; Based on the mathematical model of the flexible load unit, the environmental parameters, the daytime operating data of the supplemental lighting load, and the minimum daily total photosynthetically active radiation, the maximum response capacity of the supplemental lighting load is determined. Based on the maximum response capacity of interruptible loads, supplemental lighting loads, humidification loads, and temperature control loads, a resource aggregation model is established with the objective function of maximizing the response capacity of each resource aggregation body. The resource aggregation model is used to divide the flexible loads in the agricultural park into multiple resource aggregation bodies. Based on the intraday response instructions and the maximum response capacity of each resource aggregate, the intraday operating status of each flexible load within the agricultural park is determined.

2. The method for optimizing the load response of agricultural parks according to claim 1, characterized in that, The maximum response capacity of the interruptible load is the load reduction amount; the day-ahead operating data of the interruptible load includes the rated power and operating status of the interruptible load at each time point on the day-ahead; the agricultural park includes various interruptible loads; The load reduction amount for the i-th interruptible load is determined using the following formula: Among them, C ILi,re (t) represents the load reduction amount at time t for the i-th interruptible load, N ILi Let P be the number of the i-th interruptible load in the agricultural park. IL,i,n (t) represents the rated power of the nth interruptible load in the i-th interruptible load at time t, s IL,i,n (t) represents the operating state of the nth interruptible load in the i-th interruptible load at time t, s IL,i,n (t) = 1 indicates that operation has stopped, s IL,i,n (t) = 0 indicates that the system is in operation.

3. The method for optimizing the load response of agricultural parks according to claim 1, characterized in that, The mathematical model of the flexible load unit includes the power model of the supplementary lighting load, the power model of the humidification load, and the power model of the temperature control load.

4. The method for optimizing the load response of agricultural parks according to claim 3, characterized in that, Based on the mathematical model of the flexible load unit, and according to the environmental parameters, the daily operating data of the humidification load, and the daily operating data of the temperature control load, the least recursive squares method is used to determine the constant parameter values ​​of the humidification load and the temperature control load, specifically including: Integrating the humidification load power model over a set period of time yields a humidity change balance model within that period. Based on the sampling interval of the daily operating data of the humidification load and the humidification load power model, the constant parameters and time-varying parameters of the humidity change balance model are separated, and an identification expression for the time-varying parameters of the humidification load is constructed. Based on the environmental parameters and the daily operating data of the humidification load, determine the time-varying parameter values ​​of the humidification load; Based on the time-varying parameter values ​​of the humidification load and the identification expression of the time-varying parameter values ​​of the humidification load, the constant parameter values ​​of the humidification load are determined by the least recursive squares method. Integrating the temperature-controlled load power model over a set period of time yields a temperature change balance model within that period. Based on the sampling interval of the day-ahead operating data of the temperature-controlled load and the power model of the temperature-controlled load, the constant parameters and time-varying parameters of the temperature change balance model are separated, and an identification expression for the time-varying parameters of the temperature-controlled load is constructed. Based on the environmental parameters and the daily operating data of the temperature control load, determine the time-varying parameter values ​​of the temperature control load; Based on the time-varying parameter values ​​of the temperature-controlled load and the identification expression of the time-varying parameter values ​​of the temperature-controlled load, the constant parameter values ​​of the temperature-controlled load are determined using the least recursive squares method.

5. The method for optimizing the load response of agricultural parks according to claim 3, characterized in that, Based on the environmental parameters, the constant parameter values ​​of the humidification load, and the mathematical model of the flexible load unit, the maximum response capacity of the humidification load is determined, specifically including: Based on the environmental parameters and the constant parameter values ​​of the humidification load, determine the maximum continuous on time of the humidification load, the maximum continuous off time of the humidification load, and the humidification load control cycle; The maximum response capacity of the humidification load is determined based on the maximum continuous shutdown time of the humidification load, the control cycle of the humidification load, and the power model of the humidification load.

6. The method for optimizing the load response of agricultural parks according to claim 3, characterized in that, The daily operating data of the supplemental lighting load includes the operating status and minimum working duration of the supplemental lighting load at each time of the day; the number of supplemental lighting loads in the agricultural park is multiple; Based on the mathematical model of the flexible load unit, the environmental parameters, the daily operating data of the supplemental lighting load, and the minimum daily total photosynthetically active radiation, the maximum response capacity of the supplemental lighting load is determined, specifically including: For any supplementary lighting load, the power of the supplementary lighting load at each time before the day is determined based on the supplementary lighting load power model of the supplementary lighting load; The total daily photosynthetically active radiation of the supplemental lighting load is determined based on the power at each time before the supplemental lighting load, the environmental parameters, the minimum operating duration of the supplemental lighting load, and the operating status of the supplemental lighting load at each time before the supplemental lighting load. The response capacity of the supplemental lighting load is determined based on its power, the total daily photosynthetically active radiation of the supplemental lighting load, and the minimum daily photosynthetically active radiation. The maximum response capacity of the supplementary lighting load is determined based on the response capacity of each supplementary lighting load.

7. The method for optimizing the load response of agricultural parks according to claim 1, characterized in that, The constraints of the resource aggregation model include: load aggregation frequency constraints, load coupling constraints, response capacity constraints, response rate constraints, and greenhouse environment condition constraints.

8. The method for optimizing the load response of agricultural parks according to claim 1, characterized in that, Multiple resource aggregates are sorted from largest to smallest according to their maximum response capacity; the intraday response instructions refer to the total intraday demand response capacity. Based on the intraday response commands and the maximum response capacity of each resource aggregate, the intraday operating status of each flexible load within the agricultural park is determined, specifically including: For resource aggregates 1 to a, determine whether the sum of the maximum response capacities of resource aggregates 1 to a is greater than or equal to the total daily demand response capacity. If so, control the operation of each humidification load, each temperature control load, and each interruptible load within resource aggregates 1 to a to stop operating. Control the daily operating status of each humidification load based on the humidification load control cycle, control the daily operating status of each temperature control load based on the temperature control load control cycle, and control the operation of supplementary lighting loads with a response capacity greater than 0 within resource aggregates 1 to a to stop operating. If not, determine whether the sum of the maximum response capacities of resource aggregates 1 to a+1 is greater than or equal to the total daily demand response capacity, until all resource aggregates participate in scheduling control. At this time, the actual daily response capacity is the sum of the maximum response capacities of resource aggregates 1 to A, where a = 1, 2, ... A, and A is the number of resource aggregates.

9. A load response optimization system for agricultural parks, characterized in that, The agricultural park load response optimization system includes: The data acquisition unit is used to acquire environmental parameters of the agricultural park, daily operating data of each flexible load within the agricultural park, and characteristic parameters of each flexible load; the flexible loads within the agricultural park include supplemental lighting loads, humidification loads, temperature control loads, and interruptible loads; An interruptible capacity determination unit, connected to the data acquisition unit, is used to determine the maximum response capacity of the interruptible load based on the day-ahead operating data of the interruptible load. The individual model building unit, connected to the data acquisition unit, is used to build a mathematical model of the flexible load individual based on the environmental parameters and the characteristic parameters of each flexible load in the agricultural park; the mathematical model of the flexible load individual is used to characterize the mapping relationship between the power of each flexible load in the agricultural park and the environmental parameters. The constant parameter determination unit is connected to the single-unit model establishment unit and is used to determine the constant parameter values ​​of the humidification load and the constant parameter values ​​of the temperature control load based on the mathematical model of the flexible load single unit, according to the environmental parameters, the daily operating data of the humidification load and the daily operating data of the temperature control load, using the least recursive square method. A humidification capacity determination unit, connected to the constant parameter determination unit, is used to determine the maximum response capacity of the humidification load based on the environmental parameters, the constant parameter values ​​of the humidification load, and the mathematical model of the flexible load unit. A temperature control capacity determination unit, connected to the constant parameter determination unit, is used to determine the maximum response capacity of the temperature control load based on the environmental parameters, the constant parameter values ​​of the temperature control load, and the mathematical model of the flexible load unit. The supplementary lighting capacity determination unit, connected to the single-unit model establishment unit, is used to determine the maximum response capacity of the supplementary lighting load based on the mathematical model of the flexible load unit, the environmental parameters, the daytime operating data of the supplementary lighting load, and the minimum value of the daily total photosynthetically active radiation. The resource aggregation unit, connected to the interruptible capacity determination unit, the humidification capacity determination unit, the temperature control capacity determination unit, and the supplementary lighting capacity determination unit, is used to establish a resource aggregation model based on the maximum response capacity of the interruptible load, the maximum response capacity of the supplementary lighting load, the maximum response capacity of the humidification load, and the maximum response capacity of the temperature control load, with the maximum response capacity of each resource aggregation body as the objective function; the resource aggregation model is used to divide the flexible loads in the agricultural park into multiple resource aggregation bodies. The load response unit, connected to the resource aggregation unit, is used to determine the daily operating status of each flexible load in the agricultural park based on the daily response command and the maximum response capacity of each resource aggregation.

10. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the agricultural park load response optimization method according to any one of claims 1 to 8.

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