Simulation model of large distributed photovoltaic-ice storage air conditioning system and modeling method

Abstract

The invention relates to a simulation model of a large distributed photovoltaic-ice storage air conditioning system and a modeling method. The modeling method comprises the following steps that meteorological parameters of the place where the large distributed photovoltaic-ice storage air conditioning system is located are collected; generating hourly cooling load of the ice storage air-conditioning system through regional comprehensive energy scheme optimization and economic evaluation software; according to the meteorological parameters and the hourly cold load, the area, the arrangement mode and the generating capacity of the solar photovoltaic panels are confirmed; setting parameters of the ice storage air-conditioning system according to the meteorological parameters and the hourly cooling load data; according to the time intervals of refrigeration, ice storage and ice release in the simulated operation state of the ice storage air-conditioning system, a control strategy is set through a related forced function; and according to the simulation operation result of the large distributed photovoltaic-ice storage air conditioning system, the generating capacity of the solar photovoltaic panel, the cold storage capacity of the ice storage device, the inlet and outlet temperature, the refrigerating capacity of the refrigerating machine and the inlet and outlet temperature are output.

Description

Technical Field

[0001] The present application relates to the technical field of integration of photovoltaic consumption and ice storage air conditioning system, and more specifically, to a modeling method of a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system. Background Art

[0002] At present, the system model and modeling simulation method based on TRNSYS software mainly target single solar energy system or air conditioning system, and do not involve the system form combining distributed photovoltaic and ice storage air conditioning system. As a result, when designers design such systems, they can only design the photovoltaic panel area and ice storage device capacity respectively according to the load, resulting in the designed photovoltaic panel area and ice storage device capacity being too large, which cannot well reflect the technical and economic advantages of distributed photovoltaic and ice storage air conditioning systems compared to conventional solar air conditioning or ice storage air conditioning systems. Based on the complexity of distributed photovoltaic and ice storage air conditioning systems, the present invention proposes a simulation model and modeling method for a large-scale distributed photovoltaic-ice storage air conditioning system.

[0003] Existing technologies such as the Chinese patent with patent number "CN108061344A" disclose an optimized coordinated control system and method for distributed household photovoltaic + ice storage. The method uses a photovoltaic + ice storage air conditioning system to store solar energy in the form of ice during the day, realizes the collection of multiple low-energy-density photovoltaics, and releases cold energy at night. According to the characteristics of big data and historical data, the control system determines the ice storage amount and the working mode of the system on the day to meet the user's nighttime cooling needs. The photovoltaic + ice storage air conditioning system operation mode solves the current power supply difficulties caused by the large proportion of air conditioning load, while improving the utilization rate of photovoltaics. In addition, ice is stored during the daytime low electricity consumption period when residents go out, and cold energy is released during the peak electricity consumption at night, realizing peak shaving and valley filling, reducing the peak-valley difference in electricity consumption, stabilizing the electricity load on the grid side, and improving the power generation efficiency of the generator set. In addition, the solar energy during the day is stored for use at night, solving the problem of photovoltaic reverse peak regulation.

[0004] The problems with the above prior art are:

[0005] 1. This method relies on the characteristics of big data and historical data to determine ice storage and working methods. If the data is inaccurate or abnormal conditions occur, it may lead to wrong decisions.

[0006] 2. The operation mode of this method system is relatively complex, involving the coordinated control of multiple links such as photovoltaics, ice storage, and air conditioning. This places high demands on the stability and reliability of the control system. Once a control failure occurs, the entire system may not operate normally. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a simulation model and a modeling method for a large-scale distributed photovoltaic-ice storage air-conditioning system.

[0008] The technical solution of the present invention is as follows:

[0009] The present invention proposes a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system, comprising the following steps:

[0010] Step S1, using meteorological data software to collect meteorological parameters of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system;

[0011] Step S2, generating hourly cooling load of the ice storage air conditioning system through regional comprehensive energy solution optimization and economic evaluation software;

[0012] Step S3, confirming the area, arrangement and power generation of the solar photovoltaic panels according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system;

[0013] Step S4, setting the parameters of the ice storage air conditioning system according to the meteorological parameters and hourly cooling load data of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system, wherein the parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters;

[0014] Step S5, according to the cooling, ice storage and ice release time intervals under the simulated operation state of the ice storage air conditioning system, the control strategy is set through the forcing function of the correlation;

[0015] Step S6, according to the simulation operation results of the large-scale distributed photovoltaic-ice storage air conditioning system, output the power generation of the solar photovoltaic panel, the cold storage capacity and inlet and outlet temperatures of the ice storage device, and the cooling capacity and inlet and outlet temperatures of the refrigerator.

[0016] As a preferred implementation, the area, arrangement and power generation of the solar photovoltaic panels are determined according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system; wherein the solar photovoltaic panels are calculated by the IV performance curve of the solar photovoltaic panels, and the IV performance curve calculation formula of the solar photovoltaic panels is as follows:

[0017]

[0018] Where I is the output current of the solar photovoltaic panel, V is the output voltage of the solar photovoltaic panel, R s is the series resistance of the solar photovoltaic panel, and γ is the empirical value of the PV fitting curve, I L is the photocurrent of the solar photovoltaic panel, I ois the reverse saturation current of the diode, k is the Boltzmann constant, q is the electron charge constant, T C is the ambient temperature.

[0019] As a preferred embodiment, the parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters, among which the area F of the plate heat exchanger, the heat transfer capacity Q of the plate heat exchanger, the fouling correction coefficient β on the heat transfer surface, and the logarithmic mean temperature difference Δt pj And the expression for determining the heat transfer coefficient K is:

[0020]

[0021] In the formula, Q is the heat transfer capacity of the plate heat exchanger, β is the fouling correction factor, K is the heat transfer coefficient, Δt pj is the logarithmic mean temperature difference.

[0022] As a preferred implementation, according to the operating conditions of the ice storage air conditioning system, the control strategy is set by using the relevant forcing function of the cooling, ice storage and ice release time intervals. The relevant control strategy forcing function is as follows:

[0023] Ice storage condition forcing function:

[0024] The daily cumulative value of the predicted load is Q predict , the total capacity of the ice storage device is Q strrage , the ice production is Q ice ;

[0025] When Q predict >Q strrage , ice making capacity Q ice =Q strrage , the forcing function of ice storage condition is as follows:

[0026]

[0027] Where: [t1, t2] is the ice storage time interval;

[0028] When Q predict ≤Q strrage , ice making capacity Q ice =Q predict , the forcing function of ice storage condition is as follows:

[0029]

[0030] De-icing condition forcing function:

[0031] The cooling load of the ice storage air conditioning system is Q 1oad (t), the cooling capacity of the host under air-conditioning conditions is Q host-air, the cooling capacity of the ice storage device is Q ice-release ;

[0032] The forcing function for the host cold storage without external cooling is:

[0033] Q load (t) = 0;

[0034] Q ice-release =0;

[0035]

[0036] When Q ice (t)≤Q host-air When the main engine provides cooling alone, the forcing function is:

[0037] Q ice-release =0;

[0038]

[0039] Where, [t3, t4] is the time interval when the host provides cooling alone;

[0040] When Q load (t)<Q ice-release，max When the ice storage device releases the maximum cooling capacity, the forcing function of the ice storage device for cooling alone is:

[0041] Q host-air (t) = 0;

[0042]

[0043] Where, [t5, t6] is the time interval during which the ice storage device provides cooling alone;

[0044] When Q load (t)>Q host-air And Q load (t)>Q ice-release，max When the cooling capacity of the main engine is Q host-part The cooling capacity of the ice storage device is Q ice-part , and Q load (t) = Q host-part +Q ice-part , the forcing function of the host and ice storage device combined cooling condition is:

[0045]

[0046] Wherein, [t7, t8] is the time interval during which the host and the ice storage device jointly provide cooling.

[0047] As a preferred embodiment, the photocurrent of the solar photovoltaic panel is linearly related to the incident radiation, and the reverse saturation current of the diode is a temperature-related quantity; the specific calculation formula is as follows:

[0048]

[0049] In the formula, G T is the total radiation incident on the photovoltaic array, G T，ref is the solar radiation under reference conditions, I L，ref is the photocurrent under reference conditions;

[0050]

[0051] Where, T C is the temperature of the solar photovoltaic panel, T C，ref is the temperature of the solar photovoltaic panel under reference conditions, I O，ref is the reverse saturation current of the diode under reference conditions.

[0052] On the other hand, the present invention also provides a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system, including a meteorological parameter input module, an hourly cooling load module, a photovoltaic panel power generation module, an ice storage air conditioning system refrigeration module, a system operation control module and a simulation result output module, wherein:

[0053] The meteorological parameter input module is used to collect meteorological parameters of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system and set the meteorological parameters of the simulation model;

[0054] The hourly cooling load module is used to generate the hourly cooling load of the ice storage air conditioning system through the regional comprehensive energy solution optimization and economic evaluation software;

[0055] The photovoltaic panel power generation module determines the area, arrangement and power generation of the solar photovoltaic panels according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system;

[0056] The ice storage air conditioning system refrigeration module sets the ice storage air conditioning system parameters according to the meteorological parameters and hourly cooling load data of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system. The ice storage air conditioning system parameters include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters;

[0057] The system operation control module sets the control strategy through a correlation forcing function according to the cooling, ice storage and ice release time intervals under the simulated operation state of the ice storage air conditioning system;

[0058] The simulation result output module outputs the power generation of the solar photovoltaic panel, the cold storage capacity and inlet and outlet temperatures of the ice storage device, and the cooling capacity and inlet and outlet temperatures of the refrigerator according to the simulation operation results of the large-scale distributed photovoltaic-ice storage air conditioning system.

[0059] As a preferred embodiment, the photovoltaic panel power generation module determines the area, arrangement and power generation of the solar photovoltaic panel according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system; wherein, the solar photovoltaic panel is calculated by the IV performance curve of the solar photovoltaic panel, and the IV performance curve calculation formula of the solar photovoltaic panel is as follows:

[0060]

[0061] Where I is the output current of the solar photovoltaic panel, V is the output voltage of the solar photovoltaic panel, R s is the series resistance of the solar photovoltaic panel, and γ is the empirical value of the PV fitting curve, I L is the photocurrent of the solar photovoltaic panel, I O is the reverse saturation current of the diode, k is the Boltzmann constant, q is the electron charge constant, T C is the ambient temperature.

[0062] As a preferred embodiment, in the refrigeration module of the ice storage air conditioning system, the parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters, among which the area F of the plate heat exchanger, the heat exchange capacity Q of the plate heat exchanger, the fouling correction coefficient β on the heat transfer surface, and the logarithmic mean temperature difference Δt pj And the expression for determining the heat transfer coefficient K is:

[0063]

[0064] In the formula, Q is the heat transfer capacity of the plate heat exchanger, β is the fouling correction factor, K is the heat transfer coefficient, Δt pj is the logarithmic mean temperature difference.

[0065] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system as described in any embodiment of the present invention is implemented.

[0066] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air-conditioning system as described in any embodiment of the present invention.

[0067] The present invention has the following beneficial effects:

[0068] 1. The present invention uses TRNSYS software to design a method for modeling and simulating a large-scale distributed photovoltaic-ice storage air-conditioning system. The results show that the simulation results of the distributed photovoltaic-ice storage air-conditioning system model provided by the present invention are in good agreement with the actual operation of the distributed photovoltaic-ice storage air-conditioning system, fully demonstrating the specific conditions of the system operation.

[0069] 2. The present invention provides a strong basis for the design, optimization and evaluation of large-scale distributed photovoltaic-ice storage air-conditioning systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 Schematic diagram of the process of the present invention

[0072] Figure 2 This is a schematic diagram of the simulation model architecture of the large-scale distributed photovoltaic-ice storage air conditioning system of the present invention;

[0073] Figure 3 This is a schematic diagram of the simulation results of the simulation model of the present invention running throughout the year;

[0074] Figure 4 It is a schematic diagram of simulation results of a typical day operation of the simulation model of the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0077] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0078] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0079] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0080] Embodiment 1:

[0081] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the specific embodiments of the present application and refer to the attached Figure 1 , clearly and completely describe the technical solution of the present invention.

[0082] In order to solve the problems in the prior art, the present invention provides a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system. Figure 2 This is a system simulation model established according to a large-scale solar distributed photovoltaic-ice storage air conditioning system. To establish this large-scale solar distributed photovoltaic-ice storage air conditioning system, it is necessary to set the meteorological parameter input part, the hourly cooling load part, the photovoltaic panel power generation part, the ice storage device part, the dual-condition refrigeration host part, the base load chiller part, the plate heat exchanger part, the circulating water pump part, the system operation control part and the simulation result output part.

[0083] Step S1, using meteorological data software to collect meteorological parameters of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system.

[0084] In this embodiment, Meteonorm is used to generate an epw meteorological parameter file of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system.

[0085] Step S2, generating the hourly cooling load of the ice storage air conditioning system through regional comprehensive energy solution optimization and economic evaluation software.

[0086] In this example, the typical daily peak load of the energy station is about 48.6MW, the valley load is about 12MW, and the converted maximum cooling load index is 57.2W / m 2 .

[0087] Step S3, confirming the area, arrangement and power generation of the solar photovoltaic panels according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system;

[0088] In this embodiment, the photovoltaic panel inclination is set to 13° according to the local geographic latitude and altitude information, and the azimuth angle is 0°, that is, the south direction. The number of photovoltaic panels is obtained by dividing the total power required for system operation by the power generation power of a single panel after considering the coefficient of 1.65.

[0089] The IV performance curve of the solar photovoltaic panel changes with the change of solar radiation and ambient temperature. The solar photovoltaic panel model is based on the environmental conditions and the four module constants I of the solar photovoltaic panel. L，ref ,I O，ref ,γ,R s Generate an IV performance curve where the circuit's current I-voltage V equation is as follows:

[0090]

[0091] In the formula, I L，ref is the photocurrent under reference conditions, R s is the series resistance of the module of the solar photovoltaic panel, and γ is the empirical value of the PV fitting curve, I O is the reverse saturation current of the diode, k is the Boltzmann constant, q is the electron charge constant, T C is the ambient temperature, I O，ref is the reverse saturation current of the diode under reference conditions.

[0092] The photocurrent of a solar photovoltaic panel is linearly related to the incident radiation. The specific calculation formula is as follows:

[0093]

[0094] In the formula, G T is the total radiation incident on the photovoltaic array, G T,ref It is the solar radiation under reference conditions. This value is determined by sample data and is generally taken as 1000W / m 2 , I L,ref is the photocurrent under reference conditions.

[0095] Diode reverse saturation current I o is a temperature-dependent quantity:

[0096]

[0097] Among them, T C is the module temperature, T C，ref It is the module temperature under reference conditions. This value is determined by sample data and is generally 25°C.

[0098] Step S4, setting the parameters of the ice storage air conditioning system according to the meteorological parameters and hourly cooling load data of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system, wherein the parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters;

[0099] In this embodiment, the volume of the ice storage device is 2800m 3 The ambient temperature is determined by the local meteorological data over the years.

[0100] In this embodiment, the dual-mode chiller has a cooling capacity of 5275kW under ice-making condition, a chilled water outlet temperature of -2°C, and a cooling water inlet temperature of 30°C; and a cooling capacity of 8290kW under refrigeration condition, a chilled water outlet temperature of 4°C, and a cooling water inlet temperature of 32°C.

[0101] In this embodiment, the cooling capacity of the base load chiller is 4220 kW, the chilled water outlet temperature is 6°C, and the cooling water inlet temperature is 32°C.

[0102] In this embodiment, the area F of the heat exchanger, the heat exchange capacity Q of the heat exchanger, the fouling correction coefficient β on the heat transfer surface, and the logarithmic mean temperature difference Δt pj And the expression for determining the heat transfer coefficient K is;

[0103]

[0104] In the formula, the heat exchanger heat transfer Q is based on the actual heat exchange value, the fouling correction coefficient β is based on experience and is 0.7, and the heat transfer coefficient K is based on experience and is 5000W / (m 2 *℃), logarithmic mean temperature difference Δt of dual-condition cooling plate heat exchanger pj The value is 5.77℃, and the logarithmic mean temperature difference Δt of the ice melting plate heat exchanger is pj The value is 2.73℃.

[0105] The expression of logarithmic mean temperature Δtpj is:

[0106]

[0107] In the formula, Δt a , Δt b They are the maximum and minimum temperature differences between the heat transfer medium and the heat transferred medium, respectively.

[0108] The calculated area of ​​the dual-condition cooling plate heat exchanger is 1231m 2 The area of ​​the ice melting plate heat exchanger is 1726m 2 .

[0109] In this embodiment, the circulating water flow rate on the load side is determined according to the hourly cooling load. According to different operation modes, 470m 3 / h, 1800m 3 / h, 3780m 3 / h, the ice melting circulation water flow rate is determined according to the amount of ice released, taking 5670m 3 / h, the ice storage circulating water flow rate is determined according to the ice making capacity, taking 4500m 3 / h, the refrigeration circulation water flow rate is determined according to the cooling load, the chilled water pump flow rate of the dual-mode host refrigeration host refrigeration condition and the base load host refrigeration condition is 4500m 3 / h,1995m 3 / h, the cooling water pump flow rate is 5700m 3 / h, 3000m 3 / h.

[0110] Step S5, according to the operating conditions of the ice storage air conditioning system, the control strategy is set by using the relevant forcing function for the cooling, ice storage and ice release time intervals. The relevant control strategy forcing function is as follows:

[0111] Ice storage condition forcing function:

[0112] The daily cumulative value of the predicted load is Q predict , the total capacity of the ice storage device is Q strrage , the ice production is Q ice ;

[0113] When Q predict >Q strrage , ice making capacity Q ice =Q strrage , the forcing function of ice storage condition is as follows:

[0114]

[0115] Where: [t1, t2] is the ice storage time interval;

[0116] When Q predict ≤Q strrage , ice making capacity Q ice =Q predict , the forcing function of ice storage condition is as follows:

[0117]

[0118] De-icing condition forcing function:

[0119] The cooling load of the ice storage air conditioning system is Q 1oad (t), the cooling capacity of the host under air-conditioning conditions is Q host-air , the cooling capacity of the ice storage device is Q ice-release ;

[0120] The forcing function for the host cold storage without external cooling is:

[0121] Q load (t) = 0;

[0122] Q ice-release =0;

[0123]

[0124] When Q ice (t)≤Q host-air When the main engine provides cooling alone, the forcing function is:

[0125] Q ice-release =0;

[0126]

[0127] Where, [t3, t4] is the time interval when the host provides cooling alone;

[0128] When Q load (t)<Q ice-release，max When the ice storage device releases the maximum cooling capacity, the forcing function of the ice storage device for cooling alone is:

[0129] Q host-air (t) = 0;

[0130]

[0131] Where, [t5, t6] is the time interval during which the ice storage device provides cooling alone;

[0132] When Q load (t)>Q host-air And Q load (t)>Q ice-release，max When the cooling capacity of the main engine is Q host-part The cooling capacity of the ice storage device is Q ice-part , and Q load (t) = Q host-part +Q ice-part , the forcing function of the host and ice storage device combined cooling condition is:

[0133]

[0134] Wherein, [t7, t8] is the time interval during which the host and the ice storage device jointly provide cooling.

[0135] The relevant control strategies are as follows:

[0136] Ice storage conditions: The ice-making and cold storage conditions at night are relatively stable. When the daily cumulative value of the predicted load is greater than the total capacity of the ice storage device, ice-making and cold storage are carried out according to the maximum capacity of the ice storage device; otherwise, ice-making and cold storage are carried out according to the daily cumulative value of the predicted load on that day.

[0137] Ice release conditions: Different load distributions in each period correspond to four basic operating conditions of ice storage systems, namely, host storage, host cooling alone, ice storage device cooling alone, and host and ice storage device combined cooling. The basic operating strategies are shown in the following table:

[0138] Table 1 Operation strategy of large-scale solar distributed-ice storage air conditioning system

[0139]

[0140] Step S6, according to the simulation operation results of the large-scale distributed photovoltaic-ice storage air conditioning system, output the power generation of the solar photovoltaic panel, the cold storage capacity and inlet and outlet temperatures of the ice storage device, and the cooling capacity and inlet and outlet temperatures of the refrigerator.

[0141] The expression of the power generation of the photovoltaic panel, which is the characteristic quantity of the data output, is:

[0142] P = U × I;

[0143] Where U is voltage and I is current;

[0144] The heat exchange rate between the water in the ice storage device and the refrigerant in the ice coil is expressed as:

[0145] Q hx =Effectiveness×M hx，in ×C p，hx ×(T in，hx -T tank );

[0146] In the formula, Q hx is the heat exchange rate between the water in the ice storage tank and the refrigerant in the ice coil, Effectiveness is the heat transfer coefficient of the ice coil, M hx，in is the refrigerant inlet flow rate inside the ice coil, C p，hx is the specific heat of the refrigerant inside the ice coil, T in，hx is the refrigerant inlet temperature inside the ice coil, T tank is the temperature inside the ice storage tank.

[0147] The coefficient of performance (COP) of the chiller, which is the characteristic quantity of the data output, is expressed as:

[0148]

[0149] Among them, Q metis the cooling capacity, P is the electrical power;

[0150] Data output characterization quantity Chiller chilled water outlet temperature T chw，out The expression is:

[0151]

[0152] Among them, T chw，in is the inlet temperature of chilled water, m chw is the flow rate of chilled water.

[0153] The output results of the simulation model of the distributed photovoltaic-ice storage air conditioning system are as follows: Figure 3 and Figure 4 As shown, Figure 3 Schematic diagram of the simulation results of the system running throughout the year in an embodiment of the present invention; Figure 4 Schematic diagram of a typical day operation simulation result of the system in an embodiment of the present invention.

[0154] Embodiment 2:

[0155] This embodiment provides a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system, which is characterized by comprising a meteorological parameter input module, an hourly cooling load module, a photovoltaic panel power generation module, an ice storage air conditioning system refrigeration module, a system operation control module and a simulation result output module, wherein:

[0156] The meteorological parameter input module is used to collect meteorological parameters of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system and set the meteorological parameters of the simulation model;

[0157] The hourly cooling load module is used to generate the hourly cooling load of the ice storage air conditioning system through the regional comprehensive energy solution optimization and economic evaluation software;

[0158] The photovoltaic panel power generation module determines the area, arrangement and power generation of the solar photovoltaic panels according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system;

[0159] The ice storage air conditioning system refrigeration module sets the ice storage air conditioning system parameters according to the meteorological parameters and hourly cooling load data of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system. The ice storage air conditioning system parameters include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters;

[0160] The system operation control module sets the control strategy through a correlation forcing function according to the cooling, ice storage and ice release time intervals under the simulated operation state of the ice storage air conditioning system;

[0161] The simulation result output module outputs the power generation of the solar photovoltaic panel, the cold storage capacity and inlet and outlet temperatures of the ice storage device, and the cooling capacity and inlet and outlet temperatures of the refrigerator according to the simulation operation results of the large-scale distributed photovoltaic-ice storage air conditioning system.

[0162] Embodiment three:

[0163] This embodiment provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system as described in any embodiment of the present invention is implemented.

[0164] Embodiment 4:

[0165] This embodiment provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air-conditioning system as described in any embodiment of the present invention.

[0166] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0167] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0168] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0169] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0170] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system, wherein the simulation model of the large-scale distributed photovoltaic-ice storage air conditioning system is constructed based on the TRNSYS platform, and is characterized in that: The steps include: Step S1, using meteorological data software to collect meteorological parameters of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system; Step S2, generating hourly cooling load of the ice storage air conditioning system through regional comprehensive energy solution optimization and economic evaluation software; Step S3, confirming the area, arrangement and power generation of the solar photovoltaic panels according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system; Step S4, setting the parameters of the ice storage air conditioning system according to the meteorological parameters and hourly cooling load data of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system, wherein the parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters; Step S5, according to the cooling, ice storage and ice release time intervals under the simulated operation state of the ice storage air conditioning system, the control strategy is set through the forcing function of the correlation; Step S6, according to the simulation operation results of the large-scale distributed photovoltaic-ice storage air conditioning system, output the power generation of the solar photovoltaic panel, the cold storage capacity and inlet and outlet temperatures of the ice storage device, and the cooling capacity and inlet and outlet temperatures of the refrigerator.

2. The modeling method of a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system according to claim 1 is characterized in that: According to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system, the area, arrangement and power generation of the solar photovoltaic panels are determined; wherein, the IV performance curve of the solar photovoltaic panels is calculated by the IV performance curve of the solar photovoltaic panels, and the IV performance curve calculation formula of the solar photovoltaic panels is as follows: Where I is the output current of the solar photovoltaic panel, V is the output voltage of the solar photovoltaic panel, R s is the series resistance of the solar photovoltaic panel, and γ is the empirical value of the PV fitting curve, I L is the photocurrent of the solar photovoltaic panel, I O is the reverse saturation current of the diode, k is the Boltzmann constant, q is the electron charge constant, T C is the ambient temperature.

3. The modeling method of a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system according to claim 1, characterized in that: The parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters, among which the area F of the plate heat exchanger, the heat transfer capacity Q of the plate heat exchanger, the fouling correction coefficient β on the heat transfer surface, and the logarithmic mean temperature difference Δt pj And the expression for determining the heat transfer coefficient K is: In the formula, Q is the heat transfer capacity of the plate heat exchanger, β is the fouling correction factor, K is the heat transfer coefficient, Δt pj is the logarithmic mean temperature difference.

4. The modeling method of a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system according to claim 1, characterized in that: According to the operating conditions of the ice storage air conditioning system, the control strategy is set by using the relevant forcing function of the cooling, ice storage and ice release time intervals. The relevant control strategy forcing function is as follows: Ice storage condition forcing function: The daily cumulative value of the predicted load is Q predict , the total capacity of the ice storage device is Q strrage , the ice production is Q ice ; When Q predict >Q strrage , ice making capacity Q ice =Q strrage , the forcing function of ice storage condition is as follows: Where: [t1, t2] is the ice storage time interval; When Q predict ≤Q strrage , ice making capacity Q ice =Q predict , the forcing function of ice storage condition is as follows: De-icing condition forcing function: The cooling load of the ice storage air conditioning system is Q load (t), the cooling capacity of the host under air-conditioning conditions is Q host-air , the cooling capacity of the ice storage device is Q ice-release ; The forcing function for the host cold storage without external cooling is: Q load (t)=0; Q ice-release ＝0； When Q ice (t)≤Q host-air When the main engine provides cooling alone, the forcing function is: Q ice-release ＝0； In the formula, [t3, t4] is the time interval when the host provides cooling alone; When Q load (t) ice-release,max When the ice storage device releases the maximum cooling capacity, the forcing function of the ice storage device for cooling alone is:​ Q host-air (t)=0; Where, [t5, t6] is the time interval during which the ice storage device provides cooling alone; When Q load (t)>Q host-air And Q load (t)>Q ice-release,max When the cooling capacity of the main engine is Q host-part The cooling capacity of the ice storage device is Q ice-part ,and Q load (t) = Q host-part +Q ice-part , the forcing function of the host and ice storage device combined cooling condition is: Where [t7, t8] is the time interval during which the host and the ice storage device jointly provide cooling.

5. The modeling method of a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system according to claim 2, characterized in that: The photocurrent of the solar photovoltaic panel is linearly related to the incident radiation, and the reverse saturation current of the diode is a temperature-related quantity; the specific calculation formula is as follows: In the formula, G T is the total radiation incident on the photovoltaic array, G T,ref is the solar radiation under reference conditions, I L,ref is the photocurrent under reference conditions; Where, T C is the temperature of the solar photovoltaic panel, T C,ref is the temperature of the solar photovoltaic panel under reference conditions, I O,ref is the reverse saturation current of the diode under reference conditions.

6. A simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system, characterized in that: It includes meteorological parameter input module, hourly cooling load module, photovoltaic panel power generation module, ice storage air conditioning system refrigeration module, system operation control module and simulation result output module, among which: The meteorological parameter input module is used to collect meteorological parameters of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system and set the meteorological parameters of the simulation model; The hourly cooling load module is used to generate the hourly cooling load of the ice storage air conditioning system through the regional comprehensive energy solution optimization and economic evaluation software; The photovoltaic panel power generation module determines the area, arrangement and power generation of the solar photovoltaic panels according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system; The ice storage air conditioning system refrigeration module sets the ice storage air conditioning system parameters according to the meteorological parameters and hourly cooling load data of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system. The ice storage air conditioning system parameters include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters; The system operation control module sets the control strategy through the forcing function of correlation according to the cooling, ice storage and ice release time intervals under the simulated operation state of the ice storage air conditioning system; The simulation result output module outputs the power generation of the solar photovoltaic panel, the cold storage capacity and inlet and outlet temperatures of the ice storage device, and the cooling capacity and inlet and outlet temperatures of the refrigerator according to the simulation operation results of the large-scale distributed photovoltaic-ice storage air conditioning system.

7. The simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system according to claim 6, characterized in that: The photovoltaic panel power generation module determines the area, arrangement and power generation of the solar photovoltaic panel according to the meteorological parameters and hourly cooling load of the location of the large-scale solar distributed photovoltaic-ice storage air conditioning system; wherein the solar photovoltaic panel is calculated by the IV performance curve of the solar photovoltaic panel, and the IV performance curve calculation formula of the solar photovoltaic panel is as follows: Where I is the output current of the solar photovoltaic panel, V is the output voltage of the solar photovoltaic panel, R s is the series resistance of the solar photovoltaic panel, and γ is the empirical value of the PV fitting curve, I L is the photocurrent of the solar photovoltaic panel, I O is the reverse saturation current of the diode, k is the Boltzmann constant, q is the electron charge constant, T C is the ambient temperature.

8. The modeling method of a simulation model of a large-scale distributed photovoltaic-ice storage air conditioning system according to claim 6, characterized in that: In the refrigeration module of the ice storage air conditioning system, the parameters of the ice storage air conditioning system include: ice storage device parameters, base load chiller parameters, plate heat exchanger parameters, and circulating water pump parameters, among which the area F of the plate heat exchanger, the heat transfer capacity Q of the plate heat exchanger, the fouling correction coefficient β on the heat transfer surface, and the logarithmic mean temperature difference Δt pj And the expression for determining the heat transfer coefficient K is: In the formula, Q is the heat transfer capacity of the plate heat exchanger, β is the fouling correction factor, K is the heat transfer coefficient, Δt pj is the logarithmic mean temperature difference.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the modeling method of the simulation model of a large-scale distributed photovoltaic-ice storage air-conditioning system is implemented as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a modeling method for a simulation model of a large-scale distributed photovoltaic-ice storage air-conditioning system is implemented.

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