Planting type rural comprehensive energy multi-objective economic optimization method based on temperature influence

Abstract

The invention discloses a planting type rural comprehensive energy multi-target economic optimization method based on temperature influence, and belongs to the technical field of multi-energy optimization scheduling, and the method comprises the steps: constructing a load model and a crop growth simulation model of a smart agricultural greenhouse; the load model comprises an electric load model and a thermal load model; obtaining load demand data based on the load model and the crop growth simulation model, and constructing an energy equipment model of the rural comprehensive energy system based on the load demand data; constructing a multi-objective optimization model based on the energy equipment model and the crop growth simulation model; and solving the multi-objective optimization model to obtain an optimal energy equipment operation scheme and configuration parameters, thereby realizing optimal balance of energy consumption cost and economic benefit of the planting type rural comprehensive energy system. According to the method, the limitation of a traditional single-target rural comprehensive energy system optimization method is overcome, and the economic benefits of agricultural planting are innovatively brought into the optimization system.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-energy optimization scheduling, and in particular relates to a multi-objective economic optimization method for integrated energy in planting-type villages under the influence of temperature. Background Art

[0002] Traditional energy scheduling models often rely on single-objective optimization, often focusing solely on energy supply costs or system stability indicators while neglecting the coupling relationship between agricultural production and energy systems. This leads to problems such as insufficient energy-agriculture collaborative optimization and low overall system efficiency due to imbalanced multi-objective weights. Against this backdrop, there is an urgent need to develop a decision-making approach that balances energy economy, agricultural production efficiency, and multi-energy collaborative optimization. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a multi-objective economic optimization method for integrated energy of planting-type rural areas under the influence of temperature to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above objectives, the present invention provides a multi-objective economic optimization method for integrated energy in planting-type villages under the influence of temperature, comprising:

[0005] Constructing a load model and a crop growth simulation model for a smart agricultural greenhouse; the load model includes an electrical load model and a thermal load model;

[0006] Obtaining load demand data based on the load model and the crop growth simulation model, and constructing an energy equipment model of the rural integrated energy system based on the load demand data;

[0007] Constructing a multi-objective optimization model based on the energy equipment model and the crop growth simulation model;

[0008] The multi-objective optimization model is solved to obtain the optimal energy equipment operation plan and configuration parameters, and to achieve an optimal balance between energy consumption cost and economic benefits of the planting-type rural integrated energy system.

[0009] Optionally, the heat load model includes the following dynamic balance equation:

[0010] Heat balance equation of greenhouse surface soil:

[0011]

[0012] Heat balance equation of air in greenhouse:

[0013]

[0014] in, are the temperature of the air in the greenhouse and the temperature of the soil on the surface of the greenhouse at time t; cfd 、M fd are the specific heat capacity and mass of the greenhouse surface soil, respectively; is the heat energy exchanged between the greenhouse surface soil and the outside world at time t, including the heat energy transferred by the soil outside the greenhouse and the absorbed solar radiation; is the convective heat exchange between the greenhouse surface soil and the air in the greenhouse at time t; is the convective heat transfer between the greenhouse surface soil and the constant temperature soil in the deep greenhouse at time t; c air 、M air are the specific heat capacity and mass of the air in the greenhouse, respectively; The heat energy released by solar radiation to the air in the smart greenhouse at time t; is the convection heat exchange between the air inside the greenhouse and the air outside the greenhouse through the greenhouse glass at time t; is the convective heat exchange between the indoor air and the air outside the greenhouse through the electric ventilation equipment at time t; It is the heat exchange between the air in the greenhouse and the electric heating equipment in the greenhouse at time t.

[0015] Optionally, the process of constructing an electric load model includes: constructing a relationship between the power consumption of agricultural sodium lamps and the photosynthetic light quantum flux density, calculating the electric load demand of the agricultural sodium lamps based on the relationship, and combining the electric load demand of other electrical equipment other than the agricultural sodium lamps to obtain an electric load model.

[0016] Optionally, the relationship between power consumption and photosynthetic light quantum flux density is:

[0017]

[0018] Among them, P e,lamp is the power consumption, L e,lamp is the light effect of high pressure sodium lamp; e,lamp A is the conversion coefficient between the effective irradiance of high-pressure sodium lamp and the photosynthetic active light quantum flux density; f is the planting area of ​​the smart greenhouse; K e,lamp is the optical power equivalent of high pressure sodium lamp; D e,lamp is the photosynthetic photon flux density produced by the high pressure sodium lamp.

[0019] Optionally, the process of constructing the crop growth simulation model includes: selecting crop growth indicators, accumulating the difference between the hourly temperature and the lower limit temperature of crop growth and development, calculating the daily effective accumulated temperature and using it as the influencing factor of temperature on the crop growth indicator, constructing a mathematical model between the crop growth indicator and the daily effective accumulated temperature, and obtaining the crop growth simulation model.

[0020] Optionally, load demand data is obtained based on the load model and the crop growth simulation model, the load demand data including electrical load demand and thermal load demand, and an energy equipment model is constructed based on the load demand data, the energy equipment model including an energy conversion system and a multi-element energy storage system, the energy conversion system including a biomass methane thermoelectric system, and the multi-element energy storage system including heat storage equipment and electricity storage equipment.

[0021] Optionally, the multi-objective optimization model takes energy cost and output benefit as objective functions, and the constraints include power balance constraint and equipment capacity constraint.

[0022] Optionally, the energy cost objective function is:

[0023] F gh1 =C inv +C ma +C grid ;

[0024] Among them, C inv is the construction cost of rural integrated energy system, C ma is the maintenance cost of the rural integrated energy system, C grid the cost of purchasing electricity for the integrated rural energy system;

[0025] The construction cost is calculated as follows:

[0026]

[0027] Where, ζ i 、T i , ψ i are the planned capacity, life cycle and unit capacity construction cost of the i-th type of equipment respectively; N is the number of equipment types; s is the discount rate of the equipment;

[0028] Maintenance costs are calculated as follows:

[0029]

[0030] Where, P n,t is the output of the nth type of equipment at time t; The unit output maintenance cost of the nth type of equipment;

[0031] The cost of electricity purchase is calculated as follows:

[0032]

[0033] Where c t,e is the electricity price at time t, P grid,t is the amount of electricity purchased at time t.

[0034] Optional, output revenue objective function:

[0035]

[0036] Where c to is the price of the crop per kilogram; Y r' is the yield of the rth crop in the smart greenhouse; R se ' is the number of crops per year.

[0037] Optionally, the power balance constraint is:

[0038]

[0039] Where, are the electric load and heat load at time t in quarter r, respectively; are respectively the power generation power and heating power of the biogas engine at time t in quarter r, is the gas power of the biogas engine at time t in quarter r; is the gas output of the biogas digester at time t in quarter r; They represent the gas consumption power and heat production power of the biogas boiler at time t in quarter r; Respectively represent the output power of the electric storage device and the heat storage device; They represent the electricity power and heating power of the electric boiler in quarter r respectively;

[0040] The equipment capacity constraint is:

[0041]

[0042] Where: n represents the planned capacity of the nth type of energy equipment; θ n It is a Boolean variable, indicating the construction status of the equipment. 1 means construction, and 0 means no construction. The minimum and maximum planned capacity of the equipment, respectively.

[0043] The equipment climbing constraints are:

[0044]

[0045] Where, are the minimum and maximum operating power of the nth type of energy equipment respectively; is the operating power of the nth type of energy equipment at time t; Δt is the unit time; B n 、D n They are the upper and lower limits of the climbing power of the energy equipment respectively.

[0046] Compared with the prior art, the present invention has the following advantages and technical effects:

[0047] The present invention proposes a multi-objective economic optimization method for integrated energy of planting-type rural areas under the influence of temperature. First, a load model and a crop growth simulation model of a smart agricultural greenhouse are constructed; the load model includes an electrical load model and a thermal load model; then, load demand data is obtained based on the load model and the crop growth simulation model, and an energy equipment model of the rural integrated energy system is constructed based on the load demand data; then, a multi-objective optimization model is constructed based on the energy equipment model and the crop growth simulation model; finally, the multi-objective optimization model is solved to obtain the optimal energy equipment operation plan and configuration parameters, thereby achieving an optimal balance between energy consumption cost and economic benefits of the integrated energy system of planting-type rural areas.

[0048] The present invention constructs a multi-objective optimization system of "environmental control-energy supply-crop yield" for the smart greenhouse, takes the annual planning cost of energy supply and the annual yield benefit of crop yield as multiple objectives, and takes environmental control as a constraint to establish a multi-objective optimization model of the smart greenhouse system, thereby obtaining its efficient production energy utilization mode, overcoming the limitations of traditional single-objective rural integrated energy system optimization methods, and innovatively incorporating the economic benefits of agricultural planting into the optimization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0050] Figure 1 This is a framework diagram of a rural integrated energy system according to an embodiment of the present invention;

[0051] Figure 2 This is a flow chart of a multi-objective optimization algorithm for a rural integrated energy system according to an embodiment of the present invention;

[0052] Figure 3 1 is a typical daily temperature graph of an embodiment of the present invention, (a) is a typical daily temperature graph in spring, (b) is a typical daily temperature graph in summer, (c) is a typical daily temperature graph in autumn, and (d) is a typical daily temperature graph in winter;

[0053] Figure 4 Graphs of solar radiation intensity according to embodiments of the present invention, (a) is a graph of solar radiation intensity in spring, (b) is a graph of solar radiation intensity in summer, (c) is a graph of solar radiation intensity in autumn, and (d) is a graph of solar radiation intensity in winter;

[0054] Figure 5 Schematic diagram of time-of-use electricity prices according to an embodiment of the present invention, (a) is a schematic diagram of time-of-use electricity prices in spring, (b) is a schematic diagram of time-of-use electricity prices in summer, (c) is a schematic diagram of time-of-use electricity prices in autumn, and (d) is a schematic diagram of time-of-use electricity prices in winter;

[0055] Figure 6 A schematic diagram of a Pareto solution set for multi-objective optimization according to an embodiment of the present invention;

[0056] Figure 7 The daytime set temperature corresponding to the Pareto solution set of the embodiment of the present invention. DETAILED DESCRIPTION

[0057] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0058] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] Example 1

[0060] like Figure 1-7 As shown, this embodiment provides a multi-objective economic optimization method for integrated energy in planting-type villages under the influence of temperature, including:

[0061] Construct a load model and crop growth simulation model for smart agricultural greenhouses; the load model includes an electrical load model and a thermal load model;

[0062] Specifically, a smart agricultural greenhouse load model and a crop growth simulation model are constructed: an electrical load model is established using the light saturation point curve, characteristic parameters of agricultural sodium lamps, and other electrical equipment; a heat load model is established through greenhouse thermal energy flow analysis to derive the relationship between indoor temperature and greenhouse heat load and solar radiation; and a crop growth model based on the daily effective accumulated temperature method is established on the basis of satisfying other microclimate factors to derive the relationship between indoor greenhouse temperature and crop yield.

[0063] For the heat load model, it is considered that the heat energy of the smart greenhouse mainly flows dynamically between the surface soil of the greenhouse, the deep constant temperature soil of the greenhouse, the air inside the greenhouse and the air outside the greenhouse.

[0064] The heat load model includes the following dynamic balance equations:

[0065] Heat balance equation of greenhouse surface soil:

[0066]

[0067] Heat balance equation of air in greenhouse:

[0068]

[0069] in, are the temperature of the air in the greenhouse and the temperature of the soil on the surface of the greenhouse at time t; c fd 、M fd are the specific heat capacity and mass of the greenhouse surface soil, respectively; is the heat energy exchanged between the greenhouse surface soil and the outside world at time t, including the heat energy transferred by the soil outside the greenhouse and the absorbed solar radiation; is the convective heat exchange between the greenhouse surface soil and the air in the greenhouse at time t; is the convective heat transfer between the greenhouse surface soil and the constant temperature soil in the deep greenhouse at time t; c air 、M air are the specific heat capacity and mass of the air in the greenhouse, respectively; The heat energy released by solar radiation to the air in the smart greenhouse at time t; is the convection heat exchange between the air inside the greenhouse and the air outside the greenhouse through the greenhouse glass at time t; is the convective heat exchange between the indoor air and the air outside the greenhouse through the electric ventilation equipment at time t; It is the heat exchange between the air in the greenhouse and the electric heating equipment in the greenhouse at time t.

[0070] The process of constructing an electric load model includes: constructing a relationship between the power consumption of agricultural sodium lamps and the photosynthetic light quantum flux density, calculating the electric load demand of agricultural sodium lamps based on the relationship, and combining the electric load demand of other electrical equipment other than agricultural sodium lamps to obtain an electric load model.

[0071] For the electric load model, the greenhouse uses agricultural sodium lamps for supplementary lighting. The power consumption of agricultural sodium lamps is P e,lamp The photosynthetic light quantum flux density D e,lamp The relationship between them is:

[0072]

[0073] Among them, P e,lamp is the power consumption, L e,lamp is the light effect of high pressure sodium lamp; e,lamp A is the conversion coefficient between the effective irradiance of high-pressure sodium lamp and the photosynthetic active light quantum flux density; f is the planting area of ​​the smart greenhouse; K e,lamp is the optical power equivalent of high pressure sodium lamp; D e,lamp is the photosynthetic photon flux density produced by the high pressure sodium lamp.

[0074] The construction process of the crop growth simulation model includes: selecting crop growth indicators, accumulating the difference between hourly temperature and the lower limit temperature of crop growth and development, calculating the daily effective accumulated temperature and using it as the influencing factor of temperature on crop growth indicators, constructing a mathematical model between crop growth indicators and daily effective accumulated temperature, and obtaining a crop growth simulation model.

[0075] For the crop growth simulation model, taking tomatoes as an example, the changes in the number of fruits set and the longitudinal diameter growth of a single tomato inflorescence conform to the Logistic curve, and the mathematical model of its growth is:

[0076]

[0077] Y=κ·η ch y1 y2 3 ;

[0078] Where: y1 and y2 are the number of fruits set and the longitudinal diameter of the fruit stem, respectively; k1, a1, and b1 are the logistic curve model parameters for the number of fruits set in a single inflorescence of tomatoes; k2, a2, and b2 are the logistic curve model parameters for the longitudinal diameter growth of a single tomato fruit; e is a natural constant, which is 2.718 in this embodiment; Y is the single-crop yield of tomatoes; κ is the average number of inflorescences of this variety of tomatoes; η ch is the ratio coefficient between the fresh weight and longitudinal diameter of the mature tomato of this variety; ι GDD The daily effective accumulated temperature is the cumulative value of the sum of the hourly temperature and the lower limit temperature for tomato growth and development. By accumulating the temperature within a certain temperature range, the progress of plant growth and development is evaluated. The calculation method is:

[0079]

[0080] Where: ι GDD Daily effective accumulated temperature after planting; Δτ t is the accumulated temperature at time t; is the intermediate variable of calculation; τ m , τ b are the biological upper and lower temperature limits for tomato growth and development; The average hourly temperature of the greenhouse.

[0081] Obtain load demand data based on the load model and crop growth simulation model, and build an energy equipment model for the rural integrated energy system based on the load demand data;

[0082] Specifically, load demand data is obtained based on the load model and the crop growth simulation model. The load demand data includes electrical load demand and thermal load demand. An energy equipment model is constructed based on the load demand data.

[0083] Furthermore, based on the load demand of smart agricultural greenhouses, various energy equipment in the rural integrated energy system are established. The various equipment mainly include energy conversion systems and multi-energy storage systems. The energy conversion system includes biomass biogas thermal power system, and the multi-energy storage system includes heat storage equipment and electricity storage equipment. The multi-energy storage equipment and the electricity load demand and heat load demand of energy users are coupled to form a rural integrated energy system. The specific structure is as follows: Figure 1shown.

[0084] An energy supply model is constructed based on the load energy demand. The energy supply model is a biomass biogas thermal power system model, which includes a biogas engine and a biogas boiler. Its mathematical model is:

[0085]

[0086]

[0087] Where G r is the total biogas production of the biogas station in quarter r; S is the quality of the i-th type of biomass raw material fermented in the r-th quarter of the biogas station; i is the total solid content of the i-th type of biomass raw material; Y i is the gas production rate of the total solids of the fermentation material; i is the correction factor for gas production of biomass fermentation materials; Respectively represent the thermal power and electrical power output by the biogas engine; η BT ,η HL They represent the power generation efficiency and heat loss coefficient of the biogas turbine respectively; Indicates the biogas consumption of the biogas engine. Indicates the output thermal power of the biogas boiler; Indicates biogas consumption of biogas boiler; L G ,η BB They represent the calorific value of biogas and the heating efficiency of biogas boiler respectively.

[0088] Construct a multi-objective optimization model based on the energy equipment model and crop growth simulation model;

[0089] Taking the construction cost, maintenance cost and grid purchase cost of the above-mentioned energy equipment as the first goal, and the crop yield benefit of the smart greenhouse in step one as the second goal, a multi-objective function is constructed. With power balance and equipment capacity as constraints, and considering the overall impact of temperature on greenhouses, an optimization solution model for the rural integrated energy system is constructed.

[0090] Specifically, the energy cost objective function included in the model is:

[0091] F gh1 =C inv +C ma +C grid ;

[0092] Among them, C inv is the construction cost of rural integrated energy system, C ma is the maintenance cost of the rural integrated energy system, C grid the cost of purchasing electricity for the integrated rural energy system;

[0093] The construction cost is calculated as follows:

[0094]

[0095] Where, ζ i 、T i , ψ i are the planned capacity, life cycle and unit capacity construction cost of the i-th type of equipment respectively; N is the number of equipment types; s is the discount rate of the equipment;

[0096] Maintenance costs are calculated as follows:

[0097]

[0098] Where: P n,t is the output of the nth type of equipment at time t; The unit output maintenance cost of the nth type of equipment;

[0099] The cost of electricity purchase is calculated as follows:

[0100]

[0101] Where: c t,e is the electricity price at time t, P grid,t is the amount of electricity purchased at time t.

[0102] Output revenue objective function:

[0103]

[0104] Where c to is the price of the crop per kilogram; Y r' is the yield of the rth crop in the smart greenhouse; R se ' is the number of crops per year.

[0105] The model includes the following constraints, and the constraint expressions are as follows:

[0106] The power balance constraint is:

[0107]

[0108] Where, are the electric load and heat load at time t in quarter r, respectively; are respectively the power generation power and heating power of the biogas engine at time t in quarter r, is the gas power of the biogas engine at time t in quarter r; is the gas output of the biogas digester at time t in quarter r; They represent the gas consumption power and heat production power of the biogas boiler at time t in quarter r; Respectively represent the output power of the electric storage device and the heat storage device; They represent the electricity power and heating power of the electric boiler in quarter r respectively;

[0109] The equipment capacity constraint is:

[0110]

[0111] Where: n represents the planned capacity of the nth type of energy equipment; θ n It is a Boolean variable, indicating the construction status of the equipment. 1 means construction, and 0 means no construction. The minimum and maximum planned capacity of the equipment, respectively.

[0112] The equipment climbing constraints are:

[0113]

[0114] Where, are the minimum and maximum operating power of the nth type of energy equipment respectively; is the operating power of the nth type of energy equipment at time t; Δt is the unit time; B n 、D n They are the upper and lower limits of the climbing power of the energy equipment respectively.

[0115] The multi-objective optimization model is solved to obtain the optimal energy equipment operation plan and configuration parameters, and to achieve an optimal balance between energy consumption cost and economic benefits of the planting-type rural integrated energy system.

[0116] The NSGA-Ⅱ algorithm is used to solve the rural integrated energy system type, and the concepts of fast non-dominated sorting, congestion degree and congestion distance are introduced. The solution space is divided into different non-dominated frontiers and the diversity of solutions is maintained. Compared with traditional multi-objective solution algorithms, the proposed NSGA-Ⅱ algorithm can optimize multiple conflicting objectives at the same time. It can handle multi-objective optimization problems with constraints and unconstrained, has a wide range of adaptability, and can find a better Pareto frontier solution set. This enables the algorithm to provide a series of effective non-inferior solutions for rural integrated energy system decision makers to choose from, and realize the multi-objective comprehensive optimization of various indicators of the rural energy system. Figure 2 As shown in Figure 2, the steps of the NSGA-Ⅱ algorithm are as follows:

[0117] (1) Initialize the population. Initialize an initial population of size N, where each individual represents a possible solution.

[0118] (2) Non-dominated sorting and crowding calculation. When performing non-dominated sorting, each individual is assigned to a "frontier", indicating whether the individual is dominated by other individuals in the multi-target space. Specifically, individuals in the population are divided into multiple levels. Individuals at level 1 are the best individuals in the frontier, individuals at level 2 are dominated by individuals at level 1, but cannot be dominated by individuals at other levels, and so on. The crowding distance is used to indicate the relative density of an individual in its frontier. Individuals with larger crowding distances are better because they are in relatively open areas, which means that they have greater diversity in the target space. When calculating crowding, the crowding distance of individuals in each level is calculated based on their relative distance to each target.

[0119] (3) Selection, crossover, and mutation. The selection process is to construct a new parent population, and tournament selection is usually used. During the selection operation, individuals are selected in the order of non-dominated sorting. If there are multiple individuals on the same front, they are selected based on the crowding distance, and the individual with the larger crowding degree is selected. The crossover operation is used to generate new solutions, while the mutation operation helps maintain the diversity of the population and prevent premature convergence. In actual calculations, simulated binary crossover and polynomial mutation are usually used to generate new individuals.

[0120] (4) Merge populations. Merge the current population and the newly generated offspring population into a merged population with a size of 2N.

[0121] (5) Generate the next generation parent population. The merged population is processed through non-dominated sorting and crowding calculation, and the best N individuals are selected from the merged population to form a new population as the next generation parent population, entering the next round of evolution.

[0122] (6) Repeat steps 2 to 5 until the algorithm stops running when the preset stop condition is met, and output the optimization results of the rural integrated energy system.

[0123] The method of the present invention is analyzed below with reference to examples, as described below for details:

[0124] To verify the effectiveness of the present invention, this example uses the natural resource data and actual agricultural data of a city’s smart greenhouse park for analysis. 2 The smart greenhouse facilities in the area are optimized. Figure 3 As shown in Figure 2, the solar radiation intensity on a typical day is as follows: Figure 4 The specific electricity prices in this city are as follows. Figure 5As shown. The smart greenhouse is set to operate from 6:00 AM to 7:00 PM, a total of 14 time periods, with the remaining time being nighttime. The tomato variety in this case is a certain brand, with the upper and lower temperature limits for growth and development of this variety being 32°C and 10.6°C, respectively, and its market value is 10 yuan per kg.

[0125] The energy equipment planned in the simulation system mainly includes biogas boilers, biogas generator sets and multi-energy storage facilities. The parameters of each type of equipment are shown in Table 1.

[0126] Table 1

[0127]

[0128] In the optimization model, the daytime set temperature of the smart greenhouse and the output of the energy equipment in the greenhouse are optimized variables. During the optimization process, the population size is set to 200, the optimal individual coefficient is 0.3, the maximum evolutionary generation is 200, the maximum number of iterations is 5000, and the fitness function deviation is 1×10 -6 , the Pareto solution set of the optimization scheme is obtained by NSGA-Ⅱ algorithm, such as Figure 6 As shown in the figure, the negative value of the vertical axis represents the output benefit. The daytime set temperature of the first and second crops corresponding to each solution in the Pareto solution set is as follows Figure 7 shown.

[0129] To further evaluate the total cost and annual output of smart greenhouses across different optimization scenarios, four optimization scenarios from the Pareto solution set were selected and their specific results compared. Table 2 shows that compared to Scheme 1, Scheme 2, and Scheme 3, Scheme 4's electricity costs were 4,871.9 yuan, 4,322.5 yuan, and 3, respectively, but its annual output benefits were also 39,504 yuan, 22,795 yuan, and 4,782 yuan higher. This demonstrates that high output in smart greenhouses often translates to high energy consumption. Comparison of the four scenarios reveals that as the greenhouse's daytime temperature setting increases, its energy costs and output both increase.

[0130] Table 2

[0131]

[0132] This embodiment establishes a rural energy system model with mutual coordination and unified scheduling, takes greenhouses as the research object, focuses on the economy of the system, considers the impact of temperature on the energy cost and economic benefits of the rural energy system, and constructs a multi-objective rural integrated energy system optimization solution model to solve the problems of insufficient energy-agriculture collaborative optimization and low system comprehensive benefits caused by imbalance of multi-objective weights in existing technologies.

[0133] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A multi-objective economic optimization method for integrated energy in planting-type villages under the influence of temperature, characterized by: The following steps are involved: Constructing a load model and a crop growth simulation model for a smart agricultural greenhouse; the load model includes an electrical load model and a thermal load model; Obtaining load demand data based on the load model and the crop growth simulation model, and constructing an energy equipment model of the rural integrated energy system based on the load demand data; Constructing a multi-objective optimization model based on the energy equipment model and the crop growth simulation model; The multi-objective optimization model is solved to obtain the optimal energy equipment operation plan and configuration parameters, and to achieve an optimal balance between energy consumption cost and economic benefits of the planting-type rural integrated energy system.

2. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 1 is characterized in that: The heat load model includes the following dynamic balance equations: Heat balance equation of greenhouse surface soil: Heat balance equation of air in greenhouse: in, are the temperature of the air in the greenhouse and the temperature of the soil on the surface of the greenhouse at time t; c fd 、M fd are the specific heat capacity and mass of the greenhouse surface soil, respectively; is the heat energy exchanged between the greenhouse surface soil and the outside world at time t, including the heat energy transferred by the soil outside the greenhouse and the absorbed solar radiation; is the convective heat exchange between the greenhouse surface soil and the air in the greenhouse at time t; is the convective heat transfer between the greenhouse surface soil and the constant temperature soil in the deep greenhouse at time t; c air 、M air are the specific heat capacity and mass of the air in the greenhouse, respectively; The heat energy released by solar radiation to the air in the smart greenhouse at time t; is the convection heat exchange between the air inside the greenhouse and the air outside the greenhouse through the greenhouse glass at time t; is the convective heat exchange between the indoor air and the air outside the greenhouse through the electric ventilation equipment at time t; It is the heat exchange between the air in the greenhouse and the electric heating equipment in the greenhouse at time t.

3. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 1 is characterized in that: The process of constructing the electric load model includes: constructing a relationship between the power consumption of agricultural sodium lamps and the photosynthetic light quantum flux density, calculating the electric load demand of agricultural sodium lamps based on the relationship, and combining the electric load demand of other electrical equipment other than agricultural sodium lamps to obtain the electric load model.

4. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 3 is characterized in that: The relationship between power consumption and photosynthetic light quantum flux density is: Among them, P e,lamp is the power consumption, L e,lamp is the light effect of high pressure sodium lamp; e,lamp A is the conversion coefficient between the effective irradiance of high-pressure sodium lamp and the photosynthetic active light quantum flux density; f is the planting area of ​​the smart greenhouse; K e,lamp is the optical power equivalent of high pressure sodium lamp; D e,lamp is the photosynthetic photon flux density produced by the high pressure sodium lamp.

5. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 1 is characterized in that: The construction process of the crop growth simulation model includes: selecting crop growth indicators, accumulating the difference between hourly temperature and the lower limit temperature of crop growth and development, calculating the daily effective accumulated temperature and using it as the influencing factor of temperature on the crop growth indicator, constructing a mathematical model between the crop growth indicator and the daily effective accumulated temperature, and obtaining the crop growth simulation model.

6. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 1 is characterized in that: Load demand data is obtained based on the load model and the crop growth simulation model, the load demand data including electrical load demand and thermal load demand, and an energy equipment model is constructed based on the load demand data, the energy equipment model including an energy conversion system and a multi-element energy storage system, the energy conversion system including a biomass methane thermoelectric system, and the multi-element energy storage system including a heat storage device and an electricity storage device.

7. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 1 is characterized in that: The multi-objective optimization model takes energy cost and output benefit as objective functions, and the constraints include power balance constraint and equipment capacity constraint.

8. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 7 is characterized in that: The energy cost objective function is: F gh1 =C inv +C ma +C grid ; Among them, C inv is the construction cost of rural integrated energy system, C ma is the maintenance cost of the rural integrated energy system, C grid the cost of purchasing electricity for the integrated rural energy system; The construction cost is calculated as follows: Where, ζ i 、T i , ψ i are the planned capacity, life cycle and unit capacity construction cost of the i-th type of equipment respectively; N is the number of equipment types; s is the discount rate of the equipment; Maintenance costs are calculated as follows: Where, P n,t is the output of the nth type of equipment at time t; The unit output maintenance cost of the nth type of equipment; The cost of electricity purchase is calculated as follows: Where c t,e is the electricity price at time t, P grid,t is the amount of electricity purchased at time t.

9. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 7 is characterized in that: Output revenue objective function: Where c to is the price of the crop per kilogram; Y r' is the yield of the rth crop in the smart greenhouse; R se ' is the number of crops per year.

10. The multi-objective economic optimization method for integrated energy in planting-type villages under temperature influence according to claim 7 is characterized in that: The power balance constraint is: Where, are the electric load and heat load at time t in quarter r, respectively; are respectively the power generation power and heating power of the biogas engine at time t in quarter r, is the gas power of the biogas engine at time t in quarter r; is the gas output of the biogas digester at time t in quarter r; They represent the gas consumption power and heat production power of the biogas boiler at time t in quarter r; Respectively represent the output power of the electric storage device and the heat storage device; They represent the electricity power and heating power of the electric boiler in quarter r respectively; The equipment capacity constraint is: Where: n represents the planned capacity of the nth type of energy equipment; θ n It is a Boolean variable, indicating the construction status of the equipment. 1 means construction, and 0 means no construction. The minimum and maximum planned capacity of the equipment are shown respectively. The equipment climbing constraints are: Where, are the minimum and maximum operating power of the nth type of energy equipment respectively; is the operating power of the nth type of energy equipment at time t; Δt is the unit time; B n 、D n They are the upper and lower limits of the climbing power of the energy equipment respectively.

Images