CN114881306A - Agricultural energy internet operation optimization method and system considering greenhouse load regulation - Google Patents
Agricultural energy internet operation optimization method and system considering greenhouse load regulation Download PDFInfo
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Abstract
The invention provides an agricultural energy internet operation optimization method and system considering greenhouse load regulation, which comprises the following steps: acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data; acquiring heat load and electric load energy consumption data of an agricultural park in a typical day; establishing an operation optimization model comprising an objective function and a constraint condition; inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval; and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period. The provided optimized operation strategy can effectively reduce the operation cost of the agricultural park and improve the on-site consumption rate of new energy.
Description
Technical Field
The invention belongs to the technical field of internet operation, and particularly relates to an agricultural energy internet operation optimization method and system considering greenhouse load regulation.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In view of the disadvantages of high pollution and high consumption of petroleum agriculture, the application of the energy internet theory in agriculture has become a new focus of attention in the rural energy field.
The agricultural energy Internet is a novel power system taking new energy as a main body, and is characterized in that a large amount of new energy agricultural intelligent equipment is applied to a load side, the agricultural electrification level is high, and the agricultural energy Internet is an effective engineering mode for the vogue of villages. Agricultural electrification can ensure agricultural production modernization, and agricultural and energy cross-border fusion is profitable to become an effective means for improving agricultural load economic benefits. The carbon dioxide is absorbed by the agricultural park through photosynthesis, and the clean development of an agricultural energy system is effectively promoted.
The cooperative management and control of energy and agriculture become the key for promoting the construction of novel rural power systems and the deep fusion development of new energy and agriculture. Modern agriculture has the characteristic of high energy consumption, but the agricultural energy and agricultural collaborative management and control theory is basically in a blank state, so that the problem of constructing a novel power system taking new energy as a main body in the agricultural rural field is the first problem.
The uniqueness of agricultural load is not considered in the existing energy Internet theory, the requirements of modern agricultural production and economic operation of an energy system cannot be met, and the accurate control of output equipment in the agricultural production and the energy system cannot be realized by the existing Internet system or control system.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the agricultural energy internet operation optimization method considering greenhouse load regulation, and the working condition and the output of the energy equipment in each time period can be accurately output.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
in a first aspect, an agricultural energy internet operation optimization method considering greenhouse load regulation and control is disclosed, and comprises the following steps:
acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data;
acquiring heat load and electric load energy consumption data of an agricultural park in a typical day;
establishing an operation optimization model comprising an objective function and a constraint condition;
inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval;
and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period.
As a further technical scheme, the obtained illumination and temperature data are substituted into a photovoltaic power generation model to calculate the power generation capacity of a photovoltaic system;
substituting the wind speed data into the wind power generation model to obtain the power generation amount of the wind power generation model;
and substituting the photovoltaic output data and the wind power output data output by the model, the park electrical load data and the park thermal load data into the objective function and the constraint condition of the operation optimization model to obtain an optimal solution.
As a further technical solution, the decision variables of the optimization model are:
the method comprises the following steps of (1) acquiring power from an external power grid in each time interval, starting and stopping states and output of a CHP unit in each time interval, thermal output of a gas boiler in each time interval, charge and discharge power of stored energy in each time interval and output of photovoltaic power and wind power in each time interval;
and performing optimized operation of the comprehensive energy system by scheduling the variables.
As a further technical solution, the objective function is specifically:
wherein C is the total operating cost of a single day in the park; p link (i) The power of the power grid tie line at the moment i; e price (i) The electricity price at the moment i; u shape chp (i) The operation state of the CHP unit at the moment i is shown, wherein 1 represents that the unit is started at the moment i, and 0 represents that the unit is not started or keeps the same operation state as the previous moment; c start The starting cost of the CHP unit; e chp (i) The generated power of the unit; e chpcos t is the generating cost of the unit; c chp (i) The fixed cost of the unit; h boiler (i) Generating heat power for a gas boiler; c bolier The heat production cost of the boiler; p loadup And P loaddown Load power for time shifting; c up And C down The time shift cost is the load.
As a further technical solution, the constraint conditions include equality constraints and inequality constraints;
the constraints of the equation include: considering time-shiftable load constraints, power balance constraints and thermal balance constraints;
the inequality constraint includes:
considering the climbing constraint of the CHP unit, the output and climbing constraint of the gas boiler, the residual capacity and charge-discharge constraint of the electric energy storage, the residual capacity and charge-discharge constraint of the thermal energy storage, the tie line power constraint, the photovoltaic and wind power consumption constraint and the time-shifting load scheduling constraint.
In a second aspect, an agricultural energy internet operation optimization system considering greenhouse load regulation and control is disclosed, comprising:
a data acquisition module configured to: acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data;
acquiring heat load and electric load energy consumption data of an agricultural park in a typical day;
an optimization model building module configured to: establishing an operation optimization model comprising an objective function and a constraint condition;
a solving module configured to: inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval;
a scheduling module configured to: and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period.
The above one or more technical solutions have the following beneficial effects:
the agricultural energy internet operation optimization method considering greenhouse load regulation and control comprises the steps of firstly, constructing an output model of energy equipment such as photovoltaic, wind power and cogeneration in an agricultural park, an agricultural greenhouse light supplement load control model, a heating load regulation and control model and a greenhouse time-shifting load model, and constructing an agricultural 'carbon neutralization' model by combining carbon fixation characteristics of agricultural production. Secondly, with the lowest single-day operation cost of the park as an objective function, considering various equality and inequality constraints and the time-shifting property of greenhouse load, an optimized operation model is constructed, and the adjustment and control of the output of the equipment can be realized based on the operation model, so that the energy system is ensured to be in the state of optimal stability and instantaneity.
The phenomena that the operation cost of an agricultural park is too high and the consumption rate of new energy on the spot is low due to the lack of the current agricultural energy and agricultural production cooperative management and control technology are solved. The provided optimized operation strategy can effectively reduce the operation cost of the agricultural park and improve the on-site consumption rate of new energy.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a block diagram of an agricultural energy Internet;
FIG. 2 is a graph of typical daily power in summer;
FIG. 3 is a graph of typical daily power in winter;
FIG. 4 is a comparison graph of results before and after optimization in a typical day in summer;
FIG. 5 is a comparison graph of results before and after winter typical day optimization;
FIG. 6 is a graph of typical daily carbon sequestration in summer;
FIG. 7 is a graph of typical daily carbon sequestration for winter;
FIG. 8 is a flow diagram of a method of an embodiment.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
Referring to the attached figure 8, the embodiment discloses an agricultural energy internet operation optimization method considering greenhouse load regulation:
acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data;
acquiring heat load and electric load energy consumption data of an agricultural park in a typical day;
establishing an operation optimization model comprising an objective function and a constraint condition;
inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval;
and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period.
The specific acquisition data is as follows:
24 hours of light, temperature and wind speed data typical of an agricultural park; and 24 hours of campus thermal load, electrical load data for a typical day of an agricultural campus.
The illumination and temperature data are used for substituting into the photovoltaic power generation model to calculate the power generation capacity of the photovoltaic system; the wind speed data is used for substituting into the wind power generation model to calculate the power generation amount of the wind power generation model; and then substituting the photovoltaic output data, the wind power output data, the park electric load and the park heat load data output by the model into a target function and a constraint condition of the operation optimization model, and calling an optimization software package to solve the optimal solution.
The model can output the output of the energy equipment in each time period at the lowest cost, and reasonable dispatching of the output of each equipment can be realized by setting the output of the energy equipment in each time period after operation optimization, so that the lowest operation cost is realized.
The decision variables of the optimization model are as follows: the method comprises the following steps of (1) acquiring power from an external power grid in each time interval, starting and stopping states and output of a CHP unit in each time interval, thermal output of a gas boiler in each time interval, charging and discharging power of stored energy in each time interval, and output of photovoltaic power and wind power in each time interval; and the optimized operation of the comprehensive energy system of the agricultural park is realized by reasonably scheduling the variables.
When realizing cooperative control:
determining load data of a typical day according to agricultural production requirements;
determining the output of new energy according to the meteorological data of a typical day;
and constructing an objective function, determining various constraints, and solving by using Cplex.
The power load and the thermal load in the greenhouse of the embodiment can be adjusted according to the agricultural production requirement, so that the whole scheduling process is closer to the actual engineering.
The whole method flow can maximize the local consumption of new energy, and can simultaneously consume more electricity at the off-peak electricity price and consume less electricity at the peak electricity price, so that the energy consumption cost is further reduced.
Specifically, as shown in fig. 1, according to the agricultural energy internet operation optimization method considering greenhouse load regulation according to an embodiment of the present invention, the model establishment includes the following steps:
s101: establishing an energy equipment model of an energy internet of an agricultural park;
s102: establishing a greenhouse load regulation and control model of facility agriculture;
s103: establishing an agricultural carbon neutralization model of an energy internet of an agricultural park;
s104: and establishing an energy internet operation optimization scheduling model of the agricultural park.
Wherein, the energy equipment model of agricultural park energy internet includes: the system comprises a photovoltaic power generation model, a wind power generation model, a cogeneration unit model, a gas boiler model, a heat storage model and an electric energy storage model.
The greenhouse load regulation and control model for facility agriculture at least comprises: a greenhouse light supplement load model and a greenhouse heating load model. The facility agriculture greenhouse light supplement load model is related to the outside illumination intensity of the greenhouse, the greenhouse light transmittance and the artificial light supplement illumination intensity.
When the indoor illumination intensity I indoor Greater than the illumination intensity I set for the greenhouse according to the physiological requirements of crops crop In the meantime, no light is needed to supplement, and the indoor illumination intensity is shown as I indoor Calculating; when I is indoor ＜I crop In the meantime, light is supplemented, and the indoor illumination intensity is shown as I crop And (4) calculating.
Heat loss in the greenhouse is considered in the text, and heat transfer and loss Q of the enclosure structure are respectively 1 Heat loss Q of indoor and outdoor air exchange of greenhouse 2 Heat transfer and loss Q to greenhouse ground 3 (ii) a Two parts for heat production, namely solar radiation heat production Q 4 And heat Q provided by an external energy system.
The facility agricultural greenhouse 'carbon neutralization' model specifically comprises the following steps:
carbon dioxide generated by the CHP unit and the gas boiler during operation is introduced into the greenhouse for crops, so that a large amount of carbon dioxide emission can be reduced, and agricultural 'carbon neutralization' is realized. This section will model the net photosynthesis rate of the crop and the amount of carbon dioxide absorbed by the greenhouse.
Wherein the net photosynthetic rate model of the crop is related to the light intensity, temperature and carbon dioxide concentration of the facility agricultural greenhouse.
CO in greenhouse 2 The expenditure only considers two parts of cold air permeation exchange and crop photosynthetic absorption, and the volume of carbon dioxide consumed by the greenhouse in unit time is related to outdoor carbon dioxide concentration, wind speed influence factor, LAI leaf area index and net photosynthetic rate of the crop in unit leaf area.
The agricultural park energy internet operation optimization scheduling model specifically comprises an objective function and a constraint condition.
In the photovoltaic power generation model, a calculation formula of the output power of the photovoltaic array is shown as the following formula.
In the formula: p PV Is a photovoltaic arrayThe output power of the column; y is PV Rated installed capacity of the photovoltaic array; g T Is the solar incident radiation on the photovoltaic array under the current conditions; g T，STC Is the solar incident radiation under standard test conditions; alpha is alpha P Temperature coefficient of power; t is c，STC Photovoltaic array temperature under standard test conditions; f. of PV Is the derating factor of the photovoltaic array.
Wherein, T c For the photovoltaic array temperature under the current condition, the calculation formula is shown as formula (2):
in the formula: t is a The ambient temperature at which the photovoltaic array is located; t is c,NOCT Is the nominal operating temperature of the photovoltaic array; t is a,NOCT To define the ambient temperature of the NOCT; g T,NOCT Solar radiation to define NOCT; eta mp,STC Maximum power point efficiency under test conditions; tau is solar radiation transmittance; α is the solar radiation absorption rate.
The output of wind power generation is related to the wind speed. The output power of the wind power generator can be obtained according to the wind speed as shown in the following formula.
In the formula: p W The unit is the output of the wind driven generator and is MW; v. of ci To cut into the wind speed; v. of co Cutting out the wind speed; v. of r Rated wind speed is adopted, and the unit is m/s; p r Is the rated power of the wind driven generator and has the unit of MW.
The operation area of the back pressure type steam turbine set can be represented by a polyhedron, the operation area of the back pressure type steam turbine set has k limit operation points, and the generated power and the generated heat power are respectively shown as the following formulas:
in the formula: e chp (t) represents the generated power of the CHP unit at the time t; h chp (t) represents the heat production power of the CHP unit at the time t; e chp,k And H chp,k Respectively representing the power generation and the heat generation power of the CHP unit at the kth limit operating point; e chp,min And H chp,min Respectively representing the minimum generating power and the minimum heat generating power of the CHP unit; alpha is alpha k (t) is an end point coefficient of the CHP unit, and the value range is [0, 1%]。
The gas boiler is an important heating device in the modern agricultural park, and the mathematical model of the gas boiler is shown as the following formula.
H boiler (t)＝ηV(t)×HV g
In the formula: h boiler (t) is the heat production power of the gas-fired boiler at the moment t; v (t) is the volume of gas provided for the gas boiler at time t; HV (high voltage) device g Is the average lower heating value of natural gas.
In the agricultural energy Internet, electric energy storage is applied to a power supply system, and the adjustment of electric energy in time is realized through charging and discharging at different time periods; the heat storage energy is applied to a heating system, can store redundant heat and is supplemented when the heat energy is insufficient. Mathematical models of electricity and heat storage are shown below.
In the formula: SOC (i) is shown at the ithThe storage capacity of the electric energy storage in a time period; eta c The charging efficiency of the electric energy storage is represented; e charge (i) A charging power representing the electrical energy storage; e dcharge (i) Discharge power representing electrical stored energy; eta d Representing the discharge efficiency of the electrical energy storage; HE T (i) Representing the heat storage quantity of the heat energy storage in the ith time period; eta HI Represents the heat storage efficiency of heat storage energy; h charge (i) Represents the heat storage power of the heat storage energy; h dcharge (i) The heat release power representing heat storage energy; eta HO Indicating the heat rejection efficiency of the heat reservoir.
The total illumination intensity in the greenhouse is related to factors such as outdoor illumination intensity, the light transmittance of the greenhouse, the artificial supplementary illumination intensity and the like. The intensity of the light irradiated indoors from the outside is as follows:
I indoor ＝τ·I outdoor
in the formula: i is indoor The natural illumination intensity in the greenhouse; tau is the light transmittance of the greenhouse and is related to factors such as the coverage rate of the photovoltaic panel, a greenhouse covering material and the like; i is outdoor The intensity of illumination outside the greenhouse.
The intensity of the light needed to be artificially supplemented in the greenhouse is as follows:
I load ＝I crop -4.57I indoor
in the formula: i is load Supplementing light intensity for manual work; i is crop The illumination intensity is set according to the physiological requirements of crops in the greenhouse.
The calculation formula of the fill light load in the greenhouse is shown as the following formula.
In the formula: k is the chamber index of the greenhouse; l is the length of the greenhouse; w is the width of the greenhouse; h is the installation height of the light supplement lamp; h is the height of the irradiated plane.
In the formula: n for starting of light-compensating lampsThe number of the particles;
the luminous flux of the light supplement lamp is adopted; u is a light supplement utilization coefficient in the greenhouse and is obtained by table lookup according to the numerical value of K; m is a maintenance coefficient. In the metal halide lamp, 1klx is 14.4 μmol/(s · m) 2 )。
To sum up, the light supplement load of the greenhouse is as follows:
P load ＝N·P light
in the formula: p light The power of a single fill-in lamp.
The heat loss in the greenhouse is considered in the embodiment, namely three parts are respectively heat transfer and heat loss Q of the enclosure structure 1 Heat loss Q of indoor and outdoor air exchange of greenhouse 2 Heat transfer and loss Q to greenhouse ground 3 (ii) a Two parts for heat production, namely solar radiation heat production Q 4 And heat Q provided by an external energy system. The calculation formula of each part is shown as the following formula.
Q 1 ＝K·F(T crop -T o )
In the formula: k is the heat transfer coefficient of the peripheral glass of the greenhouse; f is the coverage area of the glass; t is crop Setting the temperature in the greenhouse, and determining according to the planted crops; t is o Is the ambient temperature outside the greenhouse.
Q 2 ＝ρ i ·N c ·V·c pi (T crop -T o )
In the formula: rho i The air density in the greenhouse is; n is a radical of c Ventilating the greenhouse every second; v is the greenhouse volume; c pi The constant pressure specific heat capacity of air in the greenhouse is obtained.
Q 3 ＝K p ·L p (T crop -T o )
In the formula: k p The heat transfer coefficient of the ground around the greenhouse; l is p The perimeter of the greenhouse periphery.
Q 4 ＝S·I indoor
In the formula: s is the greenhouse area.
To sum up, the heat provided by the external energy system for the greenhouse is, namely, the heating load of the greenhouse:
Q load ＝Q 1 +Q 2 +Q 3 -Q 4
for the heating load of the greenhouse, it should be noted that when Q is 1 +Q 2 +Q 3 ＜Q 4 When the temperature is maintained at T by default with a temperature reduction measure crop 。
Greenhouse loads are unique weather sensitive loads, and in view of the slowness of changes in physiological characteristics of crops, part of the greenhouse loads can be treated as flexible loads with time shifting and control. The time-shifting load of the greenhouse can satisfy the following relation:
in the formula: p loadup (i) Time-shiftable loads to transition to period i; p loaddown (j) The time-shiftable loading originally in the j period. P loadup,max The maximum value of the power of the time-shifting load is adjusted; p loaddowm,max The power maximum is adjusted downwards for time-shiftable loads.
The photosynthesis of crops in the greenhouse has a carbon fixing effect, so that the agricultural greenhouse is a typical carbon sink, carbon dioxide generated when the CHP unit and the gas boiler are operated is introduced into the greenhouse for being used by the crops, the emission of a large amount of carbon dioxide can be reduced, and the agricultural 'carbon neutralization' is realized. This section will model the net photosynthesis rate of the crop and the amount of carbon dioxide absorbed by the greenhouse.
Different crops have different net photosynthetic rates under the same environmental conditions, and the single leaf net photosynthetic rate model is shown in the following formula, taking cucumber as an example.
In the formula: r is the photosynthesis rate of the cucumber single leaf; the sum of a, b, c, d,h and k are model parameters that can be calculated by fitting experimental data; c crop The carbon dioxide concentration in the greenhouse is set to 700 μm 3 /m 3 And is maintained constant.
Suppose that the greenhouse takes CO 2 Constant concentration control mode and CO in greenhouse 2 Only two parts of cold air permeation exchange and crop photosynthetic absorption are considered for expenditure. CO consumed by the greenhouse in unit time 2 The volume is shown in the following formula.
V CO2 ＝(C crop -C o )·N c ·W i ·V+LAI·S·q pr
In the formula: c o Outdoor carbon dioxide concentration; w i Is a wind speed influence factor; LAI leaf area index; q. q.s pr Is the net photosynthetic rate per unit leaf area of the crop.
The objective function constructed in this embodiment is:
in the formula: c is the total operating cost per day in the park; p link (i) The power of the power grid tie line at the moment i; e price (i) The electricity price at the moment i; u shape chp (i) The operation state of the CHP unit at the moment i is shown, wherein 1 represents that the unit is started at the moment i, and 0 represents that the unit is not started or keeps the same operation state as the previous moment; c start The starting cost of the CHP unit; e chp (i) The generated power of the unit; e chpcos t is the generating cost of the unit; c chp (i) The fixed cost of the unit; h boiler (i) Generating heat power for a gas boiler; c bolier The heat production cost of the boiler; p loadup And P loaddown Load power for time shifting; c up And C down The time shift cost is the load.
In the equation constraint, time-shiftable load constraint, power balance constraint and thermal balance constraint need to be considered, which are respectively shown as the following formula.
P link (i)+E chp (i)+E dpv (i)+E dwt (i)+E dcharge (i)-
E charge (i)＝E load1 (i)+E load2 (i)+E load2 (i)+
P loadup (i)-P loaddown (i)
In the formula:
E dpv (i) photovoltaic power dissipated for time i; e dwt (i) Wind power consumed at moment i; e dcharge (i) The discharge power of the electric storage at the moment i; e charge (i) Charging power for the electricity storage at the moment i; e load (i) The electric load at the moment i comprises a greenhouse light supplement load P load 。
H chp (i)+H boiler (i)+H dcharge (i)-H charge (i)＝H load1 (i)+H load2 (i)+H load3 (i)
In the formula: h chp (i) The heat production power of the unit at the moment i; h dcharge (i) The heat release power of the heat storage at the moment i; h charge (i) The heat storage power of the heat storage at the moment i; h load The heat load at the moment i comprises a greenhouse heating load Q load 。
The climbing constraint of the CHP unit, the output and climbing constraint of the gas boiler, the residual capacity and charge-discharge constraint of the electric energy storage, the residual capacity and charge-discharge constraint of the thermal energy storage, the power constraint of the tie line, the photovoltaic and wind power consumption constraint and the time-shifting load scheduling constraint need to be considered in the inequality constraint. The above constraints are respectively shown as follows.
-RD c ≤E chp (i+1)-E chp (i)≤RU c
In the formula: RD c The maximum downward climbing power of the CHP unit; RU (RU) c The maximum power of the CHP unit for downward climbing.
In the formula: h max The maximum thermal power can be provided for the gas boiler; RD b Is the largest of gas-fired boilersDownward climbing power; RU (RU) b The maximum upward climbing power of the gas boiler.
In the formula: e socmin And E socmax Minimum and maximum electrical storage capacity, respectively; e chargemax And E dchargemax The maximum power of electricity storage charging and discharging is respectively.
In the formula: h socmin And H socmax Minimum and maximum electrical storage capacity, respectively; h chargemax And H dchargemax The maximum power of electricity storage charging and discharging is respectively.
0≤P link (i)≤P linkmax
In the formula: p linkmax Maximum power is transmitted for the tie line.
In the formula: e dpv (i) Photovoltaic power dissipated for time i; e pv (i) The total photovoltaic power generation at the moment i; e dwt (i) Wind power consumed at moment i; e wt (i) And the total wind power generation at the moment i.
In the implementation example, an energy equipment model of an energy internet of an agricultural park is established, and a multi-energy coupling energy system is formed by equipment such as photovoltaic equipment, wind power equipment, a cogeneration unit, a gas boiler, electricity storage equipment and heat storage equipment; a facility agricultural greenhouse load regulation and control model is established, and partial electric loads (such as irrigation loads) of an agricultural park can be time-shifted and dispatched, so that the facility agricultural greenhouse load regulation and control model is an excellent demand side response resource; an agricultural park energy internet 'agricultural carbon neutralization' model is established, photosynthesis of crops in a greenhouse has a carbon fixing effect, so that the agricultural greenhouse is a typical carbon sink place, carbon dioxide generated by a CHP unit and a gas boiler during operation is introduced into the greenhouse for the crops, a large amount of carbon dioxide emission can be reduced, and the agricultural 'carbon neutralization' is realized; the agricultural park energy internet operation optimization scheduling model is established, the minimum total operation cost of a single day in a park is taken as a target, various constraints of an agricultural park energy system are involved, the cooperative management and control of agricultural energy and agricultural production can be realized, and the operation cost of the agricultural park is effectively reduced.
By taking the energy internet of a certain agricultural park as a simulation object, photovoltaic and wind power output curves, an electric load curve and a thermal load power curve of a typical summer day and a typical winter day can be obtained according to the constructed model, partial load data in the park and local weather information, and are respectively shown in fig. 2 and 3. And solving the optimization model by calling Yalmip and Cplex program packages.
1) Typical day of summer analysis
The results before and after the operation optimization for the typical summer day are shown in fig. 4. As can be seen from fig. 4(a), in the period from 08:00 to 17:00, both photovoltaic and wind power have large output, but because the electrical load is limited in this period and the electrical storage is not efficiently matched with the wind and light output, a large amount of new energy in the system in this period is not consumed on the spot. During other periods of time, 08:00-17:00, the electrical energy for this period is mostly provided by the external grid, since the electrical storage has limited electrical energy stored and the CHP units are not turned on.
As can be seen from fig. 4(b), in the period of 08:00-17:00, the total load power curve after time shift is above the total load power curve before time shift; the time shifted total load power curve is below the time shifted total load power curve during the 17:00-24:00 time period. This shows that part of the greenhouse load in the time period of 17:00-24:00 is time shifted to 08:00-17:00, and in the time period of 08:00-17:00, which is just the time period with large photovoltaic and wind power output, the time shifting of the greenhouse load effectively promotes the on-site wind and light consumption. In addition, the wind and light output of the electricity storage system is more efficiently matched compared with that before optimization, charging is carried out in a time period with larger output, and discharging is carried out for load use under the condition of smaller output or no output.
Fig. 4(c) shows the result before (after) thermal optimization, because the heat load is smaller in typical summer days, the heat load can be satisfied by only turning on the gas boiler, and there is no difference in the operation of the thermal system before and after optimization.
2) Winter typical day analysis
The results before and after the optimization of the operation on a typical winter day are shown in fig. 5. As can be seen from FIG. 5(a), a large amount of new energy exists in the system before optimization and is not consumed on the spot, and the CHP unit is mainly used for supplying power in the time period from 00:00 to 05: 00; and in the time periods of 07:00-09:00 and 17:00-24:00, the power supply of an external power grid is mainly used. In addition, the wind turbine has no output because the wind speed of the typical day in winter does not reach the cut-in wind speed.
It can be seen from fig. 5(b) that the total load power curve after time shifting is above the total load power curve before time shifting in the time period of 00:00-05:00, 09:00-15:00 of the optimized system; the time shifted total load power curve is below the time unshifted total load power curve during the time periods 06:00-09:00, 16:00-24: 00. The reason for the above phenomenon is as follows: the electricity price in the time period of 00:00-05:00 is the low-valley electricity price, and the operation cost can be effectively reduced by shifting the load in the peak time period or the ordinary time period to the valley time period. In the time period of 09:00-15:00, the photovoltaic output is large, and loads in other photovoltaic output small or no-output time periods are shifted to the time periods, so that the local consumption of the photovoltaic is promoted, and the operation cost is reduced. In addition, before the operation of the CHP unit is compared and optimized, the output is less in the valley power rate period, and the output is greater in the peak power rate period, so that the unit operation mode can enable the agricultural park to purchase more electric energy at the valley power rate and less electric energy at the peak power rate under the condition of no new energy output, and further reduce the operation cost of the park.
Fig. 5(c) and 5(d) are the results before and after thermal optimization, respectively, and it can be seen that the CHP unit provides heat energy to the park before optimization. Because the operation of the CHP unit is closely related to the time-of-use electricity price, after the system is optimized, the heat load requirement of the park is mainly met by the gas boiler in the off-peak electricity price period, and the heat load requirement of the park is mainly met by the CHP unit in the on-peak electricity price period.
3) Analysis of carbon uptake in greenhouse
Agricultural greenhouses are a typical carbon sequestration site because of the need for crop photosynthesis to absorb a certain amount of carbon dioxide. FIGS. 6 and 7 are CO in typical summer and winter day greenhouses, respectively 2 Load and carbon pick-up curves. The explanation of the above phenomenon is as follows: 1) the maximum illumination intensity obtained by the agricultural greenhouse in the typical summer day is higher than that in the typical winter day, so that the maximum photosynthetic rate of crops in the typical summer day is higher than that in the typical winter day, and the maximum CO is higher 2 The load is also relatively greater. 2) Since the intensity of light set in the greenhouse is higher than that of the typical winter day at night, the average photosynthetic rate and average CO of the crops are increased on the typical winter day 2 The load is higher than the typical day in summer, and the carbon absorption amount is relatively larger. It can be seen that the regulation of greenhouse load has a certain influence on agricultural "carbon neutralization".
The embodiment solves the problems of high operation cost of the garden on a single day and low on-site consumption rate of new energy by providing an agricultural park energy internet optimization operation strategy considering agricultural greenhouse load regulation. Simulation results show that the proposed strategy obviously reduces the total operating cost of a single day of a park and improves the local consumption rate of photovoltaic and wind power by optimizing the output of energy equipment and the time shift of greenhouse load, and has certain guiding significance for promoting the landing implementation of the energy Internet of an agricultural park.
Example two
The present embodiment is directed to a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Example four
The purpose of this embodiment is to provide and takes into account agricultural energy internet operation optimization system of greenhouse load regulation and control, includes:
a data acquisition module configured to: acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data;
acquiring heat load and electric load energy consumption data of an agricultural park in a typical day;
an optimization model building module configured to: establishing an operation optimization model comprising an objective function and a constraint condition;
a solving module configured to: inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval;
a scheduling module configured to: and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. An agricultural energy internet operation optimization method considering greenhouse load regulation and control is characterized by comprising the following steps:
acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data;
acquiring heat load and electric load energy consumption data of an agricultural park in a typical day;
establishing an operation optimization model comprising an objective function and a constraint condition;
inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval;
and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period.
2. The agricultural energy internet operation optimization method considering greenhouse load regulation and control as claimed in claim 1, wherein the obtained illumination and temperature data are substituted into a photovoltaic power generation model to calculate the power generation capacity of a photovoltaic system;
substituting the wind speed data into the wind power generation model to obtain the power generation amount of the wind power generation model;
and substituting the photovoltaic output data and the wind power output data output by the model, the park electrical load data and the park thermal load data into the objective function and the constraint condition of the operation optimization model to obtain an optimal solution.
3. The agricultural energy internet operation optimization method considering greenhouse load regulation and control as claimed in claim 1, wherein the decision variables of the optimization model are:
the method comprises the following steps of (1) acquiring power from an external power grid in each time interval, starting and stopping states and output of a CHP unit in each time interval, thermal output of a gas boiler in each time interval, charge and discharge power of stored energy in each time interval and output of photovoltaic power and wind power in each time interval;
and performing optimized operation of the comprehensive energy system by scheduling the variables.
4. The agricultural energy internet operation optimization method considering greenhouse load regulation and control as claimed in claim 1, wherein the objective function is specifically:
wherein C is the total operating cost of a single day in the park; p link (i) The power of the power grid tie line at the moment i; e price (i) The electricity price at the moment i; u shape chp (i) The operation state of the CHP unit at the moment i is shown, wherein 1 represents that the unit is started at the moment i, and 0 represents that the unit is not started or keeps the same operation state as the previous moment; c start The starting cost of the CHP unit; e chp (i) The generated power of the unit; e chpcos t is the generating cost of the unit; c chp (i) The fixed cost of the unit; h boiler (i) Generating heat power for a gas boiler; c bolier The heat production cost of the boiler; p loadup And P loaddown Load power for time shifting; c up And C down The time shift cost is the load.
5. The agricultural energy internet operation optimization method considering greenhouse load regulation and control as claimed in claim 1, wherein the constraint conditions include equality constraints and inequality constraints;
the constraints of the equation include: considering time-shiftable load constraints, power balance constraints and thermal balance constraints;
the inequality constraint includes:
considering the climbing constraint of the CHP unit, the output and climbing constraint of the gas boiler, the residual capacity and charge-discharge constraint of the electric energy storage, the residual capacity and charge-discharge constraint of the thermal energy storage, the tie line power constraint, the photovoltaic and wind power consumption constraint and the time-shifting load scheduling constraint.
6. Consider agricultural energy internet operation optimization system of greenhouse load regulation and control, characterized by includes:
a data acquisition module configured to: acquiring illumination, temperature and wind speed data of an agricultural park in a typical day, and calculating generated photovoltaic and wind power data;
acquiring heat load and electric load energy consumption data of an agricultural park in a typical day;
an optimization model building module configured to: establishing an operation optimization model comprising an objective function and a constraint condition;
a solving module configured to: inputting the obtained data into a target function and a constraint condition for solving to obtain the size of a decision variable of each time interval;
a scheduling module configured to: and outputting the starting and stopping state of the energy equipment in each period and the output of the energy equipment in each period based on the decision variable of each period.
7. The agricultural energy internet operation optimization system considering greenhouse load regulation as claimed in claim 6, further comprising: substituting the obtained illumination and temperature data into a photovoltaic power generation model to calculate the power generation capacity of the photovoltaic system;
substituting the wind speed data into the wind power generation model to obtain the power generation amount of the wind power generation model;
and substituting the photovoltaic output data and the wind power output data output by the model, the park electrical load data and the park thermal load data into the objective function and the constraint condition of the operation optimization model to obtain an optimal solution.
8. The agricultural energy internet operation optimization method considering greenhouse load regulation and control as claimed in claim 1, wherein the decision variables of the optimization model are:
the method comprises the following steps of (1) acquiring power from an external power grid in each time interval, starting and stopping states and output of a CHP unit in each time interval, thermal output of a gas boiler in each time interval, charge and discharge power of energy storage in each time interval and output of photovoltaic power and wind power in each time interval;
and performing optimized operation of the comprehensive energy system by scheduling the variables.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method according to any of the preceding claims 1-5 are performed by the processor when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 5.
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| CN116667342B (en) * | 2023-07-27 | 2023-10-03 | 南京邮电大学 | Agricultural greenhouse temperature control load regulation and control method considering standby capability |
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