A greenhouse integrated energy system operation optimization method and system

Abstract

The application discloses a greenhouse comprehensive energy system operation optimization method and system, and belongs to the technical field of power system energy optimization, and comprises the following steps: step S1, collecting indoor temperature and humidity data, photovoltaic power generation power, battery power, and time-shiftable load; step S2, determining a light supplement strategy according to the collected data; and step S3, performing hierarchical optimization, wherein upper layer optimization is energy cost minimization, and lower layer optimization is a self-consumption maximization strategy and a total cost minimization strategy.The greenhouse comprehensive energy system operation optimization method and system adopt the method, comprehensively consider optimal configuration of light supplement lamps, cooling equipment and other loads, influence of different weather conditions on the comprehensive energy system, and influence of system carbon emission, and realize optimization of the greenhouse comprehensive energy system under three different scenes of sunny days, cloudy days (overcast days) and snowy days, and light supplement demand and time-shiftable characteristics of the greenhouse comprehensive energy system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system energy optimization, in particular to a greenhouse comprehensive energy system operation optimization method and system. BACKGROUND

[0002] In the prior art, Yang Ruijiang based on the greenhouse solar-biogas comprehensive energy system, researched the multi-energy conversion between electricity, heat and carbon, proposed a source-load collaborative optimization scheduling method, realized the increase of crop dry matter accumulation, and effectively reduced the energy consumption and electricity cost per unit of crop yield in the greenhouse. But it does not consider the optimal configuration of light supplement lamps, cooling equipment and other loads. Lu source based on the scheduling of comprehensive energy system of facility agricultural industrial park, considering the adjustable line of agricultural load, proposed a two-stage optimization scheduling method, and further improved the economic type and renewable energy consumption capacity of the park comprehensive energy system. But this research considers only one season, and does not consider the influence of other weather conditions on the comprehensive energy system. He Xin et al. combined photovoltaic output, time-of-use electricity price and multi-form cost energy storage characteristics to construct an optimization scheduling model of the facility system, taking the minimum operation cost as the objective function, and solved it by using particle swarm algorithm. The results show that the heat and water storage energy storage devices realize the maximization of local consumption of photovoltaic power, and reduce the influence of excess photovoltaic power on the stable operation of the distribution network. But this research does not consider the influence of system carbon emission. SUMMARY

[0003] The purpose of the present application is to provide a greenhouse comprehensive energy system operation optimization method and system, which comprehensively considers the optimal configuration of light supplement lamps, cooling equipment and other loads, the influence of different weather conditions on the comprehensive energy system, and the influence of system carbon emission, and realizes the optimization of greenhouse comprehensive energy system under three different scenes of sunny, cloudy (overcast) and snowy days.

[0004] To achieve the above purpose, the present application provides a greenhouse comprehensive energy system operation optimization method and system, comprising the following steps:

[0005] Step S1, collecting indoor temperature and humidity data, photovoltaic power generation power, battery power, time-shiftable load;

[0006] The indoor temperature and humidity data are obtained by temperature model and humidity model respectively. The temperature model is established based on the principle of energy balance, which is expressed as:

[0007]

[0008] Where, T air represents the internal temperature, ℃; C cap represents the heat capacity of the greenhouse, J / m 2 ℃; Qsun represents the incident radiation power, W / m 2 ; Q lamp represents the heating power of the lamp, W / m 2 ; Q cov represents the heat loss through the cover layer, W / m 2 ; Q trans represents the energy absorbed by the crop transpiration, W / m 2 ; Q vent represents the energy loss through ventilation, W / m 2 ; Q c represents the heating power, W / m 2 ; Q h represents the cooling power W / m 2 ;

[0009] The humidity model is represented as:

[0010]

[0011] where H air* represents the air vapor concentration, obtained from the following formula:

[0012]

[0013] where H trans represents the water vapor produced by plant transpiration, g / m 3 ; H cov represents the condensation of water vapor on the film, g / m 2 s; H vent represents the water vapor movement caused by ventilation, g / m 2 s; h represents the height of the greenhouse, m;

[0014] Step S2, according to the above data collected, determine the light supplement strategy;

[0015] Step S3, hierarchical optimization is carried out, wherein the upper layer optimization is the minimum energy cost, and the lower layer optimization is the maximum self-consumption strategy and the minimum total cost strategy.

[0016] Preferably, in the temperature model of step S1:

[0017] Q sun is defined as:

[0018]

[0019] wherein, represents the transmission coefficient of the cover layer, t r represents the shading rate, Q rad represents the energy of solar radiation, W / m 2 ;

[0020] Q cov is defined as:

[0021]

[0022] wherein, represents the heat transfer coefficient of the covering layer, W / m 2 °C; T out represents the outdoor temperature, °C;

[0023] Q trans is defined as:

[0024] Q trans = g e · L · (H crop - H air );

[0025] wherein, g e represents the transpiration conductivity, m / s; L represents the energy used for the evaporation of water in the leaves, J / g; H crop represents the absolute water vapor concentration at the crop level, g / m 3 ; H air represents the absolute concentration of water vapor, g / m 3 ;

[0026]

[0027] wherein, LAI represents the leaf area index; ε represents the ratio of latent heat and sensible heat content of saturated air; r b represents the boundary layer resistance, s / m; r s represents the stomatal resistance, s / m;

[0028]

[0029] wherein, H air,sat represents the saturated vapor concentration, g / m 3 ;

[0030]

[0031]

[0032] wherein, ρ represents the crop parameter; R n represents the net radiation at the crop level;

[0033] R n = 0.86(1 - e -0.7LAI )(Q sun + P E );

[0034] wherein, P E represents the energy of the illumination, W / m2 ;

[0035] Q lamp is defined as:

[0036] Q lamp = δ · P E ;

[0037] wherein δ represents a heating coefficient of the lamp;

[0038] Q vent is defined as:

[0039] Q vent = g v · τ air · Cp ,air (T air -T out ) ;

[0040] wherein Gv represents a ventilation rate, m / s; τ air represents air density, kg / m 3 ; Cp ,air represents heat capacity of air, J / kg℃.

[0041] Preferably, in the step S1 humidity model:

[0042] H trans is represented as:

[0043] H trans = g e (H crop -H air ) ;

[0044]

[0045] wherein p gc represents a coefficient for determining surface characteristics, m / (s·℃·C 1 / 3 ) ;

[0046] H vent is represented as:

[0047] H vent = g v (H air -H out ) ;

[0048] wherein g v represents ventilation rate, m / s.

[0049] Preferably, in the step S1 photovoltaic power generation power is obtained by a photovoltaic power generation model, and the photovoltaic power generation model is established, which is represented as:

[0050] Ppv =Q rad ·A pv ·δ pv [1+k(T cell -T ref )];

[0051] Among them, P pv δ represents the power output of PV per hour, in kWh; pv Indicates the efficiency of photovoltaic power generation; A pv The size of the photovoltaic array is represented by m. 2 k represents the temperature coefficient, in °C; T ref The battery temperature under reference conditions is expressed in °C; T cell Indicates the temperature of the PV cell, in °C;

[0052] T cell Represented as:

[0053] T cell =T air +0.0256×Q rad ;

[0054] Where T represents the greenhouse temperature, in °C; Q rad Represents solar radiant power, W / m 2 .

[0055] Preferably, in step S1, the battery capacity is characterized by a SOC-based model. SOC is described as the ratio of the battery's stored energy to its rated capacity. The battery's SOC is expressed as:

[0056]

[0057] Where SOC(k) represents the battery's charge at the last moment, %; SOC(0) represents the battery's initial charge, %; δ c δ represents the charging efficiency coefficient. d C represents the discharge efficiency coefficient. b P1 represents the rated capacity of the battery (kWh); P2 represents the discharge power of the greenhouse battery; P3 represents the power of the solar cell charging the battery; and P4 represents the energy supplied by the power grid to charge the battery.

[0058] Preferably, in step S1, the time-shiftable load refers to the supplementary lighting lamp, specifically as follows:

[0059]

[0060] in, The amount of electricity consumed by the supplemental lighting in one day, in kWh; P LED The power of the supplementary lighting is expressed in kW; T represents the total number of operating periods of the supplementary lighting; t ft represents the time when the light supplementing lamp ends running in the i th working period, h; t i t represents the time when the light supplementing lamp starts running in the i th working period, h; N t N represents the total length of time that should be light supplemented in the day, h.

[0061] Preferably, step S2 is specifically:

[0062] Model of the time-shiftable electric load and its constraint condition:

[0063]

[0064] wherein, t represents the power of the time-shiftable electric load at t, kW; respectively represent binary state variables of the electric load moving in and out at t; respectively represent the moving-in and moving-out power of the electric load at t, kW; respectively represent the lower limit and upper limit of the time-shiftable load amount of the time-shiftable electric load at t, kW;

[0065] The light supplementing strategy is as follows:

[0066] The carbon emission reduction model of the light supplementing of the light supplementing lamp is:

[0067]

[0068] wherein, S represents the area of the greenhouse, m 2 ; v0 represents the photosynthetic rate per unit area of the plant before light supplementing, μmolco2 / m 2 ·s; v1 represents the photosynthetic rate per unit area of the plant after light supplementing, μmolco2 / m 2 ·s; t' represents the light supplementing time, h.

[0069] Preferably, the upper layer optimization constraint condition in step S3 is:

[0070] Tem min ≤ Tem ≤ Tem max ;

[0071] Hum min ≤ Hum ≤ Hum max ;

[0072] Par min ≤ Par ≤ Par max ;

[0073]

[0074]

[0075] wherein, Tem min, Tem max represents the lower and upper limits of temperature; Hum min , Hum max represents the lower and upper limits of relative humidity; Par min , Par max represents the lower and upper limits of photosynthetically active radiation at different temperatures; Q c represents the heating power, W / m 2 ; Q h represents the cooling power, W / m 2 ; g v represents the ventilation speed, m / s; represents the upper limit of ventilation speed; represents the upper limit of cooling power; represents the upper limit of heating power;

[0076] Under the goal of reducing energy consumption, the optimization of minimizing energy cost is carried out, and the objective function M1 is represented as:

[0077] M1=S h +S v +S d ;

[0078]

[0079] wherein, S h represents the cost of greenhouse heating and cooling, yuan; S v represents the cost of greenhouse ventilation, yuan; S d represents the cost of light supplement lamp use, yuan; S represents the area of greenhouse, m 2 ; p e represents the electricity price, yuan / kWh; μ v represents the ventilation volume-ventilation power conversion coefficient; p o represents the non-peak electricity price, yuan / kWh.

[0080] Preferably, the self-consumption maximization strategy in step S3 introduces a self-consumption rate SCR index, and SCR is specifically:

[0081]

[0082] wherein, E self represents the energy used internally, E total represents the total energy consumption;

[0083] The total cost minimization strategy in step S3 is to balance the energy cost, battery aging cost and carbon emission cost, wherein the energy cost H e is:

[0084] H e =H p -Ws ;

[0085]

[0086] wherein, H p represents the cost of purchasing electricity from the power grid; W s represents the income generated by selling electricity back to the power grid; P4 represents the power supply of the power grid; P5 represents the power grid of the greenhouse;

[0087] Battery aging cost C a is represented as:

[0088] C a = (β cal + β cyc )·c n ·C b ;

[0089] wherein, β cal represents the daily aging cost, yuan; β cyc represents the periodic aging cost, yuan; c n represents the cost per unit of battery capacity, yuan / kWh; C b represents the rated capacity of the battery, kWh;

[0090] Carbon emission cost is represented as:

[0091]

[0092] wherein, represents the carbon trading cost, yuan; represents the carbon trading base price, yuan; γ represents the price growth rate; l represents the length of the carbon emission interval; E xt represents the carbon trading volume model;

[0093] The objective function of the total cost minimization strategy is:

[0094]

[0095] The application also provides a greenhouse comprehensive energy system operation optimization system, comprising:

[0096] The electricity demand module is provided with electricity flow by photovoltaic, power grid and battery, and is used for supplementing light lamp, insecticidal lamp, ventilation equipment and roller shutter motor;

[0097] The cold demand module is provided with cold energy flow by an electric refrigeration model, and the electric refrigeration model is specifically:

[0098]

[0099] wherein, represents the electric refrigeration equipment at t* Period refrigeration power, kW; COP Cool denotes the electric refrigeration device cold conversion efficiency; denotes t * Period electric refrigeration device power consumption, kW;

[0100] thermal demand module, which provides thermal energy flow by an electric heating model, specifically:

[0101]

[0102] wherein, denotes the electric heating device heat conversion efficiency at t * Period heating power, kW; COP Hot denotes the electric heating device heat conversion efficiency; denotes t * Period electric heating device power consumption, kW;

[0103] The system constraints of the greenhouse energy system operation optimization include boundary constraints, termination state constraints, power flow constraints, and power balance constraints. The SOC boundary constraint is expressed as:

[0104] SOC min ≤ SOC ≤ SOC max ;

[0105] wherein, SOC min denotes the minimum charging voltage of the battery; SOC max denotes the upper limit of the battery capacity;

[0106] The SOC termination constraint is formulated as:

[0107] SOC(24) ≤ SOC(0);

[0108] wherein, SOC(24) denotes the battery capacity at 24 o'clock, %;

[0109] The power flow constraint is expressed as:

[0110]

[0111] The power balance constraint is:

[0112] P2 + P3 + P4 = P pv ;

[0113] wherein, P pv denotes the hourly power output of the PV, kW; P3 denotes the energy provided by the photovoltaic to the greenhouse; and the power demand of the greenhouse load needs to be met:

[0114] P1 + P3 + P5 = P load ;

[0115] P1P2=0; load P1P2=0;

[0116] P1P2=0;

[0117] P1P2=0;

[0118] Therefore, the greenhouse comprehensive energy system operation optimization method and system adopt the above-mentioned method, comprehensively consider the optimal configuration of light supplement lamps, cooling equipment and other loads, the influence of different weather conditions on the comprehensive energy system, and the influence of system carbon emissions, and realize the optimization of the greenhouse comprehensive energy system under three different scenes of sunny days, cloudy days (overcast days) and snowy days, light supplement demand and time-shifting characteristics.

[0119] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0120] Figure 1 is a flowchart of an embodiment of the greenhouse comprehensive energy system operation optimization method of the present application;

[0121] Figure 2 is a structural diagram of an embodiment of the greenhouse comprehensive energy system operation optimization system of the present application;

[0122] Figure 3 is a hierarchical optimization structural diagram of an embodiment of the greenhouse comprehensive energy system operation optimization method of the present application;

[0123] Figure 4 is a self-consumption maximization strategy decision process diagram of an embodiment of the greenhouse comprehensive energy system operation optimization method of the present application. DETAILED DESCRIPTION

[0124] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings and examples.

[0125] Unless otherwise defined, the technical terms or scientific terms used in the present application shall have the usual meanings understood by those skilled in the art to which the present application belongs.

[0126] Example 1

[0127] As shown in Figure 1 , the present application provides a greenhouse comprehensive energy system operation optimization method, comprising the following steps:

[0128] Step S1, collecting indoor temperature and humidity data, photovoltaic power generation power, battery power, and time-shifting load;

[0129] The indoor temperature and humidity data are obtained by temperature model and humidity model, respectively. The temperature model is established based on the energy balance principle and is expressed as:

[0130]

[0131] wherein, T air represents the internal temperature, ℃; C cap represents the heat capacity of the greenhouse, J / m 2 ℃; Q sun represents the incident radiation power, W / m 2 ; Q lamp represents the heating power of the lamp, W / m 2 ; Q cov represents the heat loss through the covering layer, W / m 2 ; Q trans represents the energy absorbed by the crop transpiration, W / m 2 ; Q vent represents the energy loss through ventilation, W / m 2 ; Q c represents the heating power, W / m 2 ; Q h represents the cooling power W / m 2 ;

[0132] Q sun is defined as:

[0133]

[0134] wherein, represents the transmission coefficient of the covering layer, t r represents the shading rate, Q rad represents the energy of solar radiation, W / m 2 ;

[0135] Q cov is defined as:

[0136]

[0137] wherein, represents the heat transfer coefficient of the covering layer, W / m 2 ℃; T out represents the outdoor temperature, ℃;

[0138] Q trans is defined as:

[0139] Q trans = g e · L · (H crop -H air );

[0140] wherein, ge represents the transpiration conductance, m / s; L represents the energy used by the leaf to evaporate water, J / g; H crop represents the absolute water vapor concentration at the crop level, g / m 3 ; H air represents the absolute water vapor concentration, g / m 3 ;

[0141]

[0142] wherein LAI represents the leaf area index; ε represents the ratio of latent heat to sensible heat content of saturated air; r b represents the boundary layer resistance, s / m; r s represents the stomatal resistance, s / m;

[0143]

[0144] wherein H air,sat represents the saturated vapor concentration, g / m 3 ;

[0145]

[0146] wherein p represents the crop parameter; R n represents the net radiation at the crop level;

[0147] R n = 0.86(1 - e -0.7LAI )(Q sun + P E );

[0148] wherein P E represents the energy of the lighting, W / m 2 ;

[0149] Q lamp is defined as:

[0150] Q lamp = δ · P E ;

[0151] wherein δ represents the heating coefficient of the lamp;

[0152] Q vent is defined as:

[0153] Q vent = g v · τ air · C p,air (T air - T out );

[0154] wherein g vrepresents the ventilation rate, m / s; τ air represents the air density, kg / m 3 ; Cp ,air represents the heat capacity of air, J / kg℃.

[0155] The humidity model is expressed as:

[0156]

[0157] wherein H air* represents the air vapor concentration, obtained from the following formula:

[0158]

[0159] wherein H trans represents the water vapor produced by plant transpiration, g / m 3 ; H cov represents the condensation of water vapor on the film, g / m 2 s; H vent represents the water vapor movement caused by ventilation, g / m 2 s; h represents the height of the greenhouse, m;

[0160] H trans is expressed as:

[0161] H trans = g e (H crop -H air );

[0162]

[0163] wherein p gc represents the coefficient for determining the surface characteristics, m / (s·℃·C 1 / 3 );

[0164] H vent is expressed as:

[0165] H vent = g v (H air -H out );

[0166] wherein g v represents the ventilation rate, m / s.

[0167] The photovoltaic power in step S1 is obtained by a photovoltaic power generation model, and the photovoltaic power generation model is established and expressed as:

[0168] P pv = Q rad · A pv · δ pv[1 + k(T cell -T ref )];

[0169] wherein P pv represents the power output of PV per hour, kWh; δ pv represents the efficiency of photovoltaic power generation; A pv represents the size of the photovoltaic array, m 2 ; k represents the temperature coefficient, ℃; T ref represents the battery temperature under reference conditions, ℃; T cell represents the PV battery temperature, ℃;

[0170] T cell represents:

[0171] T cell = T air + 0.0256 × Q rad ;

[0172] wherein T air represents the greenhouse temperature, ℃; Q rad represents the solar radiation power, W / m 2 .

[0173] In step S1, the battery power is characterized based on the SOC model, and the SOC is described as the proportion of the storage energy of the battery to its rated capacity, and the SOC of the battery is represented as:

[0174]

[0175] wherein SOC(k) represents the power of the battery at the last time, %; SOC(0) represents the initial power of the battery, %; δ c represents the charging efficiency coefficient; δ d represents the discharging efficiency coefficient; C b represents the rated capacity of the battery, kWh; P1 represents the discharging power of the greenhouse battery; P2 represents the power of the solar battery charging the battery; P6 represents the energy of the grid charging the battery.

[0176] In step S1, the time-shifting load refers to the light supplement lamp, which is specifically:

[0177]

[0178] wherein, is the power consumed by the light supplement lamp in a day, kWh; P LED represents the power of the light supplement lamp, kW; T represents the total number of working time periods of the light supplement lamp; t f represents the time at which the light supplement lamp ends operation in the i th working time period, h; t ih represents the time when the i th working period of the light supplement lamp starts to run, h; N t h represents the total length of time that should be lighted up for the day, h.

[0179] Step S2, according to the collected data, determine the light supplement strategy, specifically:

[0180] Model of time-shiftable electric load and its constraint conditions:

[0181]

[0182] wherein, h represents the time when the i th working period of the light supplement lamp starts to run, h; N respectively represent binary state variables of the time-shiftable electric load at t time; respectively represent the time-shiftable electric load at t time; respectively represent the lower limit and upper limit of the time-shiftable load amount of the time-shiftable electric load at t time, kW;

[0183] The light supplement strategy is as follows: the carbon dioxide consumption amount caused by the light supplement lamp, the light supplement lamp prolongs the photosynthesis time and increases the carbon dioxide consumption amount of the plant. The energy consumption of the light supplement lamp and its influence on photosynthesis determine the carbon dioxide consumption amount.

[0184] The carbon emission reduction model of the light supplement lamp is:

[0185]

[0186] wherein, S represents the area of the greenhouse, m 2 ; v0 represents the photosynthetic rate per unit area of the plant before light supplement, μmolco2 / m 2 ·s; v1 represents the photosynthetic rate per unit area of the plant after light supplement, μmolco2 / m 2 ·s; t' represents the light supplement time, h.

[0187] Step S3, hierarchical optimization, the upper layer transmits the optimal energy demand to the lower layer through optimization, and the lower layer further optimizes the scheduling of the greenhouse comprehensive energy system on the basis of the optimization result of the upper layer, and transmits the optimal scheduling to the upper layer. The hierarchical optimization structure is shown in Figure 3 . Wherein, P1 is the discharge power of the greenhouse battery, P2 is the power of the solar cell charging the battery, P3 is the energy provided by the photovoltaic to the greenhouse, P4 is the power supply of the power grid, P5 is the power supply of the greenhouse, and P6 is the energy of the power grid charging the battery.

[0188] The upper layer optimization is the minimization of energy cost, and the lower layer optimization is the maximization of self-consumption strategy and the minimization of total cost strategy.

[0189] The upper limit of the optimization constraint in step S3 is:

[0190] Tem min ≤ Tem ≤ Tem max ;

[0191] Hum min ≤ Hum ≤ Hum max ;

[0192] Par min ≤ Par ≤ Par max ;

[0193]

[0194] wherein, Tem min , Tem max denote the lower limit and upper limit of temperature; Hum min , Hum max denote the lower limit and upper limit of relative humidity; Par min , Par max denote the lower limit and upper limit of photosynthetically active radiation at different temperatures; Q c denotes heating power, W / m 2 ; Q h denotes cooling power, W / m 2 ; g v denotes ventilation speed, m / s; denotes the upper limit of ventilation speed; denotes the upper limit of cooling power; denotes the upper limit of heating power;

[0195] The optimization for minimizing energy cost is carried out under the goal of reducing energy consumption, and the objective function M1 is expressed as:

[0196] M1 = S h + S v + S d ;

[0197]

[0198] wherein, S h denotes the cost of greenhouse heating and cooling, yuan; S v denotes the cost of greenhouse ventilation, yuan; S d denotes the cost of supplemental light use, yuan; S denotes the area of greenhouse, m 2 ; p e denotes the electricity price, yuan / kWh; μ v denotes the ventilation volume-ventilation power conversion coefficient; p o denotes the non-peak electricity price, yuan / kWh.

[0199] Figure 4 A detailed representation of the self-consumption maximization strategy is provided, and its decision-making process is outlined.

[0200] The self-consumption rate SCR is introduced in step S3 of the self-consumption maximization strategy, and SCR is specifically:

[0201]

[0202] wherein E self represents the energy used internally, E total represents the total energy consumption.

[0203] The total cost minimization strategy in step S3 balances the energy cost, battery aging cost, and carbon emission cost, wherein the energy cost H e is:

[0204] H e = H p -W s ;

[0205]

[0206] wherein H p represents the cost of purchasing electricity from the grid; W s represents the income generated by selling electricity back to the grid; P4 represents the power supply of the grid; and P5 represents the grid of the greenhouse.

[0207] The battery aging cost C a is represented as:

[0208] C a = (β cal + β cyc )·c n ·C b ;

[0209] wherein β cal represents the daily aging cost, yuan; β cyc represents the periodic aging cost, yuan; c n represents the cost per unit of battery capacity, taking into account the initial investment and maintenance cost, yuan / kWh; and C b represents the rated capacity of the battery, kWh.

[0210] β cal (t) = 6.6148 × 10 -6 × SOC(t) + 4.6404 × 10 -6 ;

[0211]

[0212] wherein L cycThe life cycle of the battery is represented.

[0213] Carbon emission cost Is represented as:

[0214]

[0215] Wherein, The carbon trading cost is represented by Yuan; The carbon trading base price is represented by Yuan; gamma represents the price growth rate; l represents the length of the carbon emission interval; E xt The carbon trading volume model is represented as:

[0216] E xt = E st -E g ;

[0217] E st = E gt -E pv -C DC ;

[0218]

[0219] Wherein, E st The actual carbon emission of the system is represented by kg; E gt The actual carbon emission of the purchased upper grid power is represented by kg; E pv The actual carbon emission reduced by photovoltaic is represented by kg; a1, b1, c1 represent the calculation power generation parameters of the upper grid.

[0220]

[0221] Wherein, P g The power generation power of the grid is represented by kW.

[0222] The objective function of the total cost minimization strategy is:

[0223]

[0224] The application also provides a greenhouse comprehensive energy system operation optimization system, and the structure thereof is shown in Figure 2 The greenhouse comprehensive energy system operation optimization system comprises:

[0225] The electricity demand module is provided with electricity flow by photovoltaic, grid and battery, and is used for supplementing light lamps, insecticidal lamps, ventilation equipment and roller shutter motors;

[0226] The cold demand module is provided with cold flow by an electric refrigeration model, and the electric refrigeration model is specifically:

[0227]

[0228] Wherein, represents the electrical refrigeration device in t * period refrigeration power, kW; COP Cool represents the electrical refrigeration device cold conversion efficiency; represents t * period electrical refrigeration device power consumption, kW;

[0229] thermal demand module, the thermal energy flow is provided by the electrical heating model, and the electrical heating model is specifically:

[0230]

[0231] wherein, represents the electrical refrigeration device in t * period heating power, kW; COP Hot represents the electrical heating device heat conversion efficiency; represents t * period electrical heating device power consumption, kW;

[0232] The system constraints of the greenhouse energy system operation optimization include boundary constraints, termination state constraints, power flow constraints and power balance constraints. The SOC boundary constraint is represented as:

[0233] SOC min ≤ SOC ≤ SOC max ;

[0234] wherein, SOC min represents the minimum charging voltage of the battery; SOC max represents the upper limit of the battery capacity;

[0235] The SOC termination constraint is formulated as:

[0236] SOC(24) ≤ SOC(0);

[0237] wherein, SOC(24) represents the battery capacity at 24 o'clock, %;

[0238] The power flow constraint is represented as:

[0239]

[0240] The power balance constraint is:

[0241] P2+P3+P4=P pv ;

[0242] wherein, P pv represents the hourly power output of the PV, kW; P3 represents the energy provided by the photovoltaic to the greenhouse; and the power demand of the greenhouse load needs to be met at the same time:

[0243] P1+P3+P5=Pload ;

[0244] where P load represents the power requirement of the greenhouse load, kW; the corresponding power constraint is represented as:

[0245] P1P2=0;

[0246] P1P6=0.

[0247] Therefore, the greenhouse comprehensive energy system operation optimization method and system adopt the above-mentioned comprehensive consideration of the optimal configuration of the light supplementing lamp, the cooling equipment and other loads, the influence of different weather conditions on the comprehensive energy system, and the influence of system carbon emission, so as to realize the optimization of the greenhouse comprehensive energy system under three different scenes of sunny day, cloudy day (overcast day) and snowy day.

[0248] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for optimizing operation of a greenhouse integrated energy system, characterized in that, Comprising the following steps: Step S1, collecting indoor temperature and humidity data, photovoltaic power generation, battery power, time-shiftable load; The indoor temperature and humidity data are obtained by a temperature model and a humidity model, respectively, and the temperature model is established based on the principle of energy balance, which is represented as: where T air represents the internal temperature, °C; C cap represents the greenhouse heat capacity, J / m 2 °C; Q sun represents the incident radiation power, W / m 2 ; Q lamp represents the lamp heating power, W / m 2 ; Q cov represents the heat loss through the cover, W / m 2 ; Q trans represents the energy absorbed by crop transpiration, W / m 2 ; Q vent represents the energy loss through ventilation, W / m 2 ; Q c represents the heating power, W / m 2 ; Q h represents the cooling power W / m 2 ; The humidity model is represented as: where H air,sat represents the saturated vapor concentration, g / m 3 ; H air* represents the air vapor concentration, obtained from the following equation: where H trans represents the water vapor produced by the transpiration of the plants, g / m 3 s; H cov represents the condensation of water vapor on the film, g / m 2 s; H vent represents the movement of water vapor caused by ventilation, g / m 2 s; h represents the height of the greenhouse, m; The time-shiftable load refers to a light supplement lamp, which is specifically: in, The amount of electricity consumed by the supplemental lighting in one day, in kWh; P LED The power of the supplementary lighting is expressed in kW; T represents the total number of operating periods of the supplementary lighting; t f The time (h) represents the end time of the i-th working period of the supplementary lighting; t represents the time when the supplementary lighting stops operating. i Indicates the start time of the i-th working period of the supplementary lighting, h; N t The total duration of supplemental lighting required for the day, in hours; Step S2, determining a light supplement strategy according to the collected data; Step S2 is specifically: The model of the time-shiftable electric load and its constraint conditions: wherein, P(t) represents the time-shiftable electric load power at time t, kW; P(t) represents the time-shiftable electric load power at time t, kW; P(t) represents the time-shiftable electric load power at time t, kW; P(t) represents the time-shiftable electric load power at time t, kW; The light supplement strategy is as follows: The carbon dioxide consumption caused by the light supplement lamp increases the carbon dioxide consumption of the plants by prolonging the photosynthesis time; The carbon emission reduction model of the light supplement lamp is: wherein S represents the area of the greenhouse, m 2 ; v0represents the photosynthetic rate per unit area of the plant before light supplementation, pmol co2 / m 2 · s; vi represents the photosynthetic rate per unit area of the plant after light supplementation, pmol co2 / m 2 · s; t' represents the light supplementation time, h; Step S3, hierarchical optimization, in which the upper layer optimization is to minimize the energy cost, and the lower layer optimization is to maximize the self-consumption strategy and minimize the total cost strategy.

2. The method of claim 1, wherein, In the temperature model in step S1: Q sun is defined as: wherein denotes the transmission coefficient of the cover layer, t r denotes the shading factor, Q rad denotes the energy of the solar radiation, W / m 2 ; Q cov is defined as: wherein, represents the heat transfer coefficient of the covering layer, W / m 2 °C; T out represents the outdoor temperature, °C; Q trans is defined as: Q trans = g e · L · (H crop - H air ); where g e represents the transpiration conductance, m / s; L represents the energy used by the leaf to evaporate water, J / g; H crop represents the absolute water vapor concentration at the crop level, g / m 3 ; H air represents the absolute concentration of water vapor, g / m 3 ; wherein LAI represents the leaf area index; ε represents the ratio of latent heat to sensible heat content of saturated air; r b represents the boundary layer resistance, s / m; r s represents the stomatal resistance, s / m; where p represents a crop parameter; R n represents the net radiation at crop level; R n = 0.86(1 - e -0.7LAI )(Q sun + P E ); where P E represents the energy of the illumination, W / m 2 ; Q lamp is defined as: Q lamp = δ · P E ; Wherein, δ represents the heating coefficient of the lamp; Q vent is defined as: Q vent = g v · τ air · C p,air (T air - T out ); where g v represents the rate of ventilation, m / s; τ air represents the air density, kg / m 3 ; C p,air represents the heat capacity of air, J / kg°C.

3. The method of claim 2, wherein, In the humidity model in step S1: H trans is represented as: H trans = g e (H crop - H air ); wherein p gc represents a coefficient for determining the surface property, m / (s °C °C 1 / 3 ); H vent is represented as: H vent = g v (H air -H out ).

4. The method of claim 3, wherein, In step S1, the photovoltaic power generation is obtained by a photovoltaic power generation model, and the photovoltaic power generation model is established, which is represented as: P pv = Q rad · A pv · δ pv [1 + k(T cell - T ref )]; where P pv represents the power output of the PV per hour, kWh; δ pv represents the efficiency of the photovoltaic generation; A pv represents the size of the photovoltaic array, m 2 ; k represents the temperature coefficient, °C; T ref represents the temperature of the cell under reference conditions, °C; T cell represents the temperature of the PV cell, °C; T cell is represented as: T cell = T air + 0.0256 x Q rad ; where T air represents the greenhouse temperature, °C; Q rad represents the solar radiation power, W / m 2 .

5. The method of claim 4, wherein, In step S1, the battery power is represented by a SOC-based model, and the SOC is described as the ratio of the stored energy of the battery to its rated capacity. The SOC of the battery is represented as: where SOC(k) represents the battery's charge at the last time, %; Soc(0) represents the initial charge of the battery, %; δ c represents the charging efficiency coefficient; δ d represents the discharging efficiency coefficient; C b represents the rated capacity of the battery, kWh; P1 represents the discharging power of the greenhouse battery; P2 represents the power of the solar battery charging the battery; P6 represents the energy of the grid charging the battery.

6. The method of claim 5, wherein, In step S3, the constraint condition of the upper layer optimization is: T emmin ≤ Tem≤ Tem max ; Hum min ≤ Hum ≤ Hum max ; Par min ≤ Par ≤ Par max ; Among them, Tem min Tem max Indicates the lower and upper limits of temperature; Hum min Hum max Indicates the lower and upper limits of relative humidity; Par min Par max Q represents the lower and upper limits of photosynthetically active radiation at different temperatures; c This indicates heating power, W / m 2 Q h This indicates cooling power, W / m 2 ; Indicates the upper limit of ventilation speed; Indicates the upper limit of cooling power; Indicates the upper limit of heating power; Under the goal of reducing energy consumption, the optimization of minimizing the energy cost is carried out, and the objective function M1 is represented as: M1 = S h + S v + S d ; where S h represents the cost of heating and cooling the greenhouse, yuan; S v represents the cost of ventilation of the greenhouse, yuan; S d represents the cost of using the light supplement lamp, yuan; S represents the area of the greenhouse, m 2 ; p e represents the electricity price, yuan / kWh; p v represents the ventilation volume-ventilation power conversion coefficient; p o represents the non-peak electricity price, yuan / kWh.

7. The method of claim 6, wherein, In step S3, the self-consumption maximization strategy introduces a self-consumption rate SCR index, which is specifically: where E self represents the energy used internally, E total represents the total energy consumption; The total cost minimization strategy in step S3 is to balance the energy cost, battery aging cost, and carbon emission cost, wherein the energy cost H e is: H e = H p - W s ; where H p represents the cost of purchasing electricity from the grid; W s represents the income generated by selling electricity back to the grid; P4 represents the supply of electricity from the grid; P5 represents the grid for the greenhouse; Battery aging cost C a is expressed as: C a = (β cal + β cyc ) · c n · C b ; wherein β cal represents the daily aging cost, yuan; β cyc represents the periodic aging cost, yuan; c n represents the cost per unit of battery capacity, yuan / kWh; C b represents the rated capacity of the battery, kWh; Carbon emission costs is represented as: Wherein, represents the carbon trading cost, yuan; represents the carbon trading base price, yuan; γ represents the price growth amplitude; l represents the carbon emission interval length; E xt represents the carbon trading volume model; The objective function of the total cost minimization strategy is:

8. A system for operating optimization of a greenhouse integrated energy system according to any one of claims 1-7, characterized in that, Comprising: The electricity demand module provides electricity flow from photovoltaic, power grid, and battery, and is used for light supplement lamp, insecticidal lamp, ventilation equipment, and roller shutter motor; The cold demand module provides cold energy flow from the electric refrigeration model, and the electric refrigeration model is specifically: wherein, represents the electrical power of the electrical refrigeration device at t * kW; COP Cool represents the electrical power of the electrical refrigeration device at t represents the electrical power of the electrical refrigeration device at t * kW; The heat demand module provides heat energy flow from the electric heating model, and the electric heating model is specifically: wherein, represents the electric heating device heat conversion efficiency at t * heating power, kW; COP Hot represents the electric heating device heat conversion efficiency; represents the electric heating device heat conversion efficiency at t * electric power consumption of the electric heating device at t The system constraints of the greenhouse energy system operation optimization include boundary constraints, termination state constraints, power flow constraints, and power balance constraints. The SOC boundary constraint is represented as: SOC min ≤ SOC ≤ SOC max ; wherein SOC min represents the minimum charging voltage of the battery; SOC max represents the upper limit of the battery capacity; The SOC termination constraint is formulated as: SOC(24)≤SOC(0); Wherein, SOC(24) represents the battery power at 24 o'clock, %; The power flow constraint is represented as: The power balance constraint is: P2+P3+P4=P pv ; where P pv represents the power output of PV per hour, kW; P3 represents the energy provided by photovoltaics to the greenhouse; while the electrical power demand of the greenhouse load needs to be met: P1+P3+P5=P load ; where P load represents the power demand of the greenhouse load, kW; the corresponding power constraint is expressed as: P1P2=0; P1P6=0.

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