Space-time matching evaluation method, system and equipment for photovoltaic output and refrigeration house load

Abstract

The invention provides a space-time matching evaluation method, system and equipment for photovoltaic output and refrigeration house load, and is applied to the technical field of load prediction. The method comprises the following steps: determining the daily output condition of a target photovoltaic on a prediction day based on the sun illumination parameter of the target photovoltaic on the prediction day and the inherent photoelectric parameter of the target photovoltaic; the total heat load of the target refrigeration house on the prediction day is determined by considering the refrigeration house enclosure structure heat load, the refrigeration house ventilation heat load, the refrigeration house cargo heat load, the refrigeration house operation heat load and the refrigeration house operation heat load, and the total power utilization load condition of the target refrigeration house on the prediction day is obtained through thermoelectric conversion; performing multi-dimensional matching on the daily output condition and the total electrical load condition, and performing matching evaluation based on a matching result of each dimension to obtain a matching evaluation result of the target photovoltaic and the target refrigeration house in each time period of the prediction day; the target refrigeration house is within the target photovoltaic power supply range. The method solves the problems that the space-time matching of the photovoltaic output and the refrigeration house load is difficult, and the matching degree is difficult to accurately evaluate.

Description

Technical Field

[0001] The present invention relates to the technical field of load forecasting, and particularly to a method, system and device for spatio-temporal matching evaluation of photovoltaic output and cold storage load. Background Art

[0002] The rapid development of distributed photovoltaic has strongly promoted the clean and low-carbon transformation of energy, but at the same time, it has also brought a series of new problems. Among them, grid connection and consumption are increasingly severe challenges faced by the further development of distributed photovoltaic in the near future, especially in rural areas with a relatively high penetration rate. On the one hand, the accessible capacity on the distribution side is limited, resulting in problems such as an increased risk of grid safe operation and a decline in power quality; on the other hand, the local consumption rate of distributed photovoltaic is low, and the consumption capacity in rural areas is limited, which does not conform to the original intention of nearby local consumption, and also reduces the economy from a system perspective.

[0003] With the rapid development of fresh agricultural product storage and preservation technology in China and the growth of market demand, the number of fresh agricultural products transported by cold storage logistics has gradually increased, and the cold storage logistics of fresh agricultural products has developed rapidly, which provides favorable conditions for the nearby local consumption of distributed photovoltaic in rural areas. However, affected by uncontrollable factors such as meteorological conditions and market prices, both photovoltaic output and cold storage load show uncertainty, resulting in great difficulty in spatio-temporal matching between the two, and it is difficult to accurately evaluate the matching degree. Summary of the Invention

[0004] In order to overcome the problems of difficult spatio-temporal matching between photovoltaic output and cold storage load and difficult accurate evaluation of the matching degree, the present invention provides a method, system and device for spatio-temporal matching evaluation of photovoltaic output and cold storage load.

[0005] On the one hand, the present invention provides a method for spatio-temporal matching evaluation of photovoltaic output and cold storage load, including:

[0006] Based on the solar illumination parameters of the target photovoltaic on the prediction day and the inherent photovoltaic parameters of the target photovoltaic, determine the daily output situation of the target photovoltaic on the prediction day;

[0007] Considering the cold storage enclosure heat load, cold storage ventilation heat load, cold storage cargo heat load, cold storage operation heat load and cold storage operation heat load, based on the cargo storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day and the inherent thermal correlation parameters of the target cold storage, determine the total heat load of the target cold storage on the prediction day;

[0008] Based on the thermoelectric conversion of the total heat load of the target cold storage on the prediction day, obtain the total electricity load situation of the target cold storage on the prediction day;

[0009] Perform multi-dimensional matching on the predicted daily output of the target photovoltaic and the total electricity load of the target cold storage on the predicted day, and conduct matching evaluation of the target photovoltaic and the target cold storage in each time period of the predicted day based on the matching results of each dimension to obtain the matching evaluation results of the target photovoltaic and the target cold storage in each time period of the predicted day;

[0010] Among them, the target cold storage is within the power supply range of the target photovoltaic.

[0011] Optionally, the solar illumination parameters of the target photovoltaic on the predicted day include the relative position of the sun relative to the target photovoltaic, and the inherent optoelectronic parameters of the target photovoltaic include the photovoltaic panel angle information of the target photovoltaic and the optoelectronic conversion parameters of the target photovoltaic. Determining the predicted daily output of the target photovoltaic based on the solar illumination parameters of the target photovoltaic on the predicted day and the inherent optoelectronic parameters of the target photovoltaic includes:

[0012] Based on the solar optical parameters on the predicted day and the position information of the target photovoltaic, calculate the relative position information of the sun relative to the target photovoltaic on the predicted day;

[0013] Based on the relative position information of the sun relative to the target photovoltaic on the predicted day and the photovoltaic panel angle information of the target photovoltaic, calculate the solar incidence angle at the target photovoltaic on the predicted day;

[0014] Based on the solar incidence angle at the target photovoltaic on the predicted day, the solar optical parameters on the predicted day, and the photovoltaic panel angle information of the target photovoltaic, calculate the solar radiation received by the target photovoltaic on the predicted day;

[0015] Based on the solar radiation received by the target photovoltaic on the predicted day and the optoelectronic conversion parameters of the target photovoltaic, calculate the predicted daily output of the target photovoltaic.

[0016] Optionally, the solar optical parameters on the predicted day include the solar declination angle on the predicted day, the optical depth on the predicted day, and the scattering factor on the predicted day. The relative position information of the sun relative to the target photovoltaic on the predicted day includes the solar altitude angle and the solar azimuth angle at the target photovoltaic on the predicted day; the calculation formula for the solar altitude angle at the target photovoltaic on the predicted day is:

[0017] β = arcsin(cosL cosδcosH + sinL sinδ);

[0018] In the formula, β is the solar altitude angle at the target photovoltaic on the predicted day, L is the geodetic latitude at the target photovoltaic, δ is the solar declination angle on the predicted day, and H is the solar hour angle at the target photovoltaic;

[0019] The calculation formula for the solar azimuth angle at the target photovoltaic on the predicted day is:

[0020]

[0021] In the formula, φ S is the solar azimuth angle at the target photovoltaic on the prediction day;

[0022] The photovoltaic panel angle information of the target photovoltaic includes the azimuth angle and tilt angle of the photovoltaic panel of the target photovoltaic. The calculation formula for the solar incident angle at the target photovoltaic on the prediction day is:

[0023] α = arccos[cosβcos(φ S - φ C )sinξ + sinβcosξ];

[0024] In the formula, α is the solar incident angle at the target photovoltaic on the prediction day, φ C is the azimuth angle of the photovoltaic panel of the target photovoltaic, and ξ is the tilt angle of the photovoltaic panel of the target photovoltaic;

[0025] The calculation formula for the solar radiation received by the target photovoltaic on the prediction day is:

[0026]

[0027] In the formula, I C is the solar radiation received by the target photovoltaic on the prediction day, I BC , I DC , I RC are the direct, diffuse, and reflected radiation amounts that can be received by the photovoltaic panel of the target photovoltaic on the prediction day respectively, I B is the direct sunlight intensity on the prediction day, I SC is the solar constant on the prediction day, k is the optical depth on the prediction day, C is the scattering factor on the prediction day, and ρ p is the reflectivity of the ground where the target photovoltaic is located to solar radiation;

[0028] The photovoltaic conversion parameters of the target photovoltaic include the area of the photovoltaic cell panel of the target photovoltaic and the conversion efficiency of the photovoltaic cell panel of the target photovoltaic. The calculation formula for the daily output situation of the target photovoltaic on the prediction day is:

[0029] P PV = A p × η × I C ;

[0030] In the formula, P PV is the daily output situation of the target photovoltaic on the prediction day, A p is the area of the photovoltaic cell panel of the target photovoltaic, and η is the conversion efficiency of the photovoltaic cell panel of the target photovoltaic.

[0031] Optionally, the inherent thermal correlation parameters of the target cold storage include the enclosure structure parameters of the target cold storage, the heat transfer parameters of the enclosure structure, and the air thermal parameters inside the target cold storage; the cargo storage plan of the target cold storage includes the types of goods stored in the target cold storage and the corresponding storage quantities, ventilation conditions, lighting conditions, door opening and closing operation conditions, in-store operation conditions of the staff, and evaporator operation conditions; considering the cold storage enclosure structure heat load, cold storage ventilation heat load, cold storage cargo heat load, cold storage operation heat load, and cold storage operation heat load, based on the cargo storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day, and the inherent thermal correlation parameters of the target cold storage, determining the total heat load of the target cold storage on the prediction day, including:

[0032] Based on the weather conditions outside the target cold storage on the prediction day, the enclosure structure parameters of the target cold storage, and the heat transfer parameters of the enclosure structure of the target cold storage, calculating the enclosure structure heat load of the target cold storage on the prediction day;

[0033] Based on the ventilation conditions of the target cold storage on the prediction day, the air thermal parameters inside the target cold storage on the prediction day, and the weather conditions outside the target cold storage on the prediction day, calculating the ventilation heat load of the target cold storage on the prediction day;

[0034] Based on the storage quantities of various types of goods in the target cold storage on the prediction day and the changes in the thermal parameters of various types of goods entering the target cold storage, calculating the cargo heat load of the target cold storage on the prediction day;

[0035] Based on the lighting conditions, door opening and closing operation conditions, and in-store operation conditions of the staff inside the target cold storage on the prediction day, calculating the operation heat load of the target cold storage on the prediction day;

[0036] Based on the evaporator operation conditions of the target cold storage on the prediction day, determining the operation heat load of the target cold storage on the prediction day;

[0037] Based on the enclosure structure heat load, ventilation heat load, cargo heat load, operation heat load, and operation heat load of the target cold storage on the prediction day, determining the total heat load of the target cold storage on the prediction day.

[0038] Optionally, the calculation formula for the total heat load of the target cold storage on the prediction day is as follows:

[0039] Q T =Q w +Q v +Q p +Q m +Q e ;

[0040] In the formula, Q T is the total heat load of the target cold storage on the prediction day, Q w is the enclosure structure heat load of the target cold storage on the prediction day, Q v is the ventilation heat load of the target cold storage on the prediction day, Qp For the thermal load of goods in the target cold storage on the prediction day, Q m For the operating thermal load of the target cold storage on the prediction day, Q e For the running thermal load of the target cold storage on the prediction day;

[0041]

[0042] Wherein, U is the total heat transfer coefficient of the enclosure structure of the target cold storage, A c Is the surface area of the enclosure structure of the target cold storage, T a Is the outdoor temperature of the target cold storage on the prediction day, T is Is the indoor temperature of the target cold storage on the prediction day, h 0 Is the outdoor air heat transfer coefficient of the target cold storage on the prediction day, h i Is the indoor surface air heat transfer coefficient of the target cold storage on the prediction day, k w Is the wall thermal conductivity of the enclosure structure of the target cold storage, Δω is the wall thickness of the enclosure structure of the target cold storage, and ν is the outdoor wind speed of the target cold storage on the prediction day;

[0043]

[0044] Wherein, n t Is the daily ventilation and air change rate of the target cold storage on the prediction day, V n Is the net volume inside the target cold storage, Δh air Is the enthalpy change of the air entering the target cold storage on the prediction day, ρ air Is the air density inside the target cold storage;

[0045]

[0046] Wherein, m j,p Is the daily incoming quantity of the j-th kind of goods on the prediction day, h j,1 , h j,2 Are respectively the specific enthalpy of the initial temperature when the j-th kind of goods enters the target cold storage and the specific enthalpy at the end of temperature reduction in the storage on the prediction day, q j,1 , q j,2 Are respectively the respiration heat per unit mass of the j-th kind of goods at the initial cooling temperature and the respiration heat per unit mass at the end temperature on the prediction day, t j,1 , t j,2 Are respectively the temperatures of the corresponding packaging materials of the j-th kind of goods when entering the target cold storage and at the end of temperature reduction on the prediction day, m j,b , c j,b Are respectively the mass and specific heat capacity of the corresponding outer packaging of the j-th kind of goods on the prediction day, τ j Is the cooling and processing time of the j-th kind of goods;

[0047]

[0048] In the formula, Q d is the lighting heat flux per unit area of the floor of the target cold storage, A d is the floor area of the target cold storage, n k is the number of cold storage doors of the target cold storage, n k ' is the number of times the cold storage door of the target cold storage is opened on the predicted day, h w and h n are the specific enthalpies of the indoor and outdoor air of the target cold storage on the predicted day respectively. M is the air curtain correction coefficient of the target cold storage, ρ n is the density of the goods stored in the target cold storage, N is the number of operators in the target cold storage on the predicted day, Q a is the average heat load released per person per unit time, t s is the working time of the operator;

[0049] Q e = N me S / μ me ;

[0050] In the formula, N me is the number of evaporator fan motors of the target cold storage, S is the shaft power of the evaporator fan motor of the target cold storage, μ me is the efficiency of the evaporator fan motor of the target cold storage.

[0051] Optionally, the total heat load of the target cold storage on the predicted day is subjected to thermoelectric conversion to obtain the total electricity load situation of the target cold storage on the predicted day, including:

[0052] Based on the condensation coefficient of the target cold storage on the predicted day obtained by fitting, the total heat load of the target cold storage on the predicted day is subjected to thermoelectric conversion to obtain the electricity consumption of the compressor of the target cold storage on the predicted day;

[0053] Based on the lighting electricity consumption, condenser motor power, evaporator motor power and compressor electricity consumption of the target cold storage on the predicted day, the total electricity load situation of the target cold storage on the predicted day is determined.

[0054] Optionally, the multi-dimensional matching of the daily output situation of the target photovoltaic on the predicted day and the total electricity load situation of the target cold storage on the predicted day includes:

[0055] Based on the correlation analysis of the daily output situation of the target photovoltaic on the predicted day and the total electricity load situation of the target cold storage on the predicted day in each time period, the fluctuation matching index in the volatility dimension of each time period on the predicted day is obtained;

[0056] Based on the ratio of the daily output situation of the target photovoltaic to the total electricity load situation at each predicted moment on the predicted day, the electricity quantity matching index in the electricity quantity dimension of each time period on the predicted day is determined.

[0057] Optionally, performing a matching evaluation of the target photovoltaic and the target cold storage in each time period of the forecast day based on the matching result of each dimension, and obtaining a matching evaluation result of the target photovoltaic and the target cold storage in each time period of the forecast day, includes:

[0058] Based on the membership function corresponding to the matching index of each dimension, the matching strength analysis is performed on the matching index of each dimension in each period of the forecast day to obtain the true value corresponding to the matching index of each dimension in each period of the forecast day;

[0059] Based on the corresponding weight of each matching index on the forecast day, the real value corresponding to the matching index of each dimension in each time period of the forecast day is weighted and integrated to obtain the matching evaluation result between the target photovoltaic and the target cold storage in each time period of the forecast day;

[0060] The weight corresponding to each matching index of the forecast day is determined based on the multi-dimensional matching results of several days adjacent to the forecast day.

[0061] Optionally, before performing a matching evaluation of the target photovoltaic and the target cold storage in each time period of the forecast day based on the matching result of each dimension and obtaining the matching evaluation result of the target photovoltaic and the target cold storage in each time period of the forecast day, the method further includes:

[0062] Obtain multi-dimensional matching results within the future target time range;

[0063] Taking the daily multi-dimensional matching results within the future target time range as an evaluation object, taking the real value corresponding to the matching index of one dimension of each day as an evaluation index of the corresponding evaluation object, and based on the multi-dimensional matching results within the future target time range, using the spread-out method to determine the corresponding weight of each matching index on the forecast day;

[0064] The future target time range includes the forecast date and several days adjacent to the forecast date.

[0065] Optionally, after performing a matching evaluation of the target photovoltaic and the target cold storage in each time period of the forecast day based on the matching result of each dimension, and obtaining the matching evaluation result of the target photovoltaic and the target cold storage in each time period of the forecast day, the method further includes:

[0066] If the matching evaluation result between the target photovoltaic and the target cold storage in each time period of the forecast day is less than a preset threshold, the operation mode of the target cold storage is adjusted.

[0067] On the other hand, the present invention also provides a time-space matching evaluation system for photovoltaic output and cold storage load, comprising:

[0068] A photovoltaic output prediction module, configured to determine the sunrise power condition of a target photovoltaic on a prediction day based on the solar illumination parameters of the target photovoltaic on the prediction day and the inherent photovoltaic parameters of the target photovoltaic;

[0069] A cold storage load prediction module, configured to consider the heat load of the cold storage enclosure structure, the ventilation heat load of the cold storage, the heat load of the cold storage goods, the operating heat load of the cold storage, and the running heat load of the cold storage, and determine the total heat load of the target cold storage on the prediction day based on the goods storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day, and the inherent thermal correlation parameters of the target cold storage; based on the thermoelectric conversion of the total heat load of the target cold storage on the prediction day, obtain the total electricity load condition of the target cold storage on the prediction day;

[0070] A matching evaluation module, configured to perform multi-dimensional matching on the sunrise power condition of the target photovoltaic on the prediction day and the total electricity load condition of the target cold storage on the prediction day, and perform matching evaluation on the target photovoltaic and the target cold storage at each time period on the prediction day based on the matching results of each dimension, so as to obtain the matching evaluation results of the target photovoltaic and the target cold storage at each time period on the prediction day;

[0071] Wherein, the target cold storage is within the power supply range of the target photovoltaic.

[0072] On the other hand, the present invention further provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected through a bus;

[0073] The memory is used to store one or more programs;

[0074] When the one or more programs are executed by the at least one processor, the spatio-temporal matching evaluation method for photovoltaic output and cold storage load described in any one of the above is implemented.

[0075] On the other hand, the present invention further provides a readable storage medium, on which an execution program is stored, and when the execution program is executed, the spatio-temporal matching evaluation method for photovoltaic output and cold storage load described in any one of the above is implemented.

[0076] Compared with the prior art, the beneficial effects of the present invention are:

[0077] The present invention provides a method and system for evaluating the spatio-temporal matching of photovoltaic output and cold storage load. By considering the heat load of the cold storage enclosure structure, the ventilation heat load of the cold storage, the heat load of the cold storage goods, the operating heat load of the cold storage, and the running heat load of the cold storage, based on the goods storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day, and the inherent thermal correlation parameters of the target cold storage, the total heat load of the target cold storage on the prediction day is determined. Combining thermoelectric conversion, by considering the enclosure structure, ventilation conditions, loading conditions, operating conditions, and running conditions of the cold storage, the accurate prediction of the total electricity load of the target cold storage is achieved, improving the accuracy of cold storage load prediction. In addition, with the prediction of the output of the target photovoltaic within the power supply range, the spatio-temporal matching potential between the cold storage load and distributed photovoltaic can be exploited, realizing the spatio-temporal matching between the agricultural product cold chain in rural areas and the nearby photovoltaic.

[0078] Through the multi-dimensional matching of the daily output of the target photovoltaic and the total electricity load of the target cold storage, and the matching evaluation of the matching results in each dimension, the matching conditions in multiple dimensions can be comprehensively considered, achieving an accurate evaluation of the matching degree between the photovoltaic and the cold storage, providing a basis for the nearby and local consumption of photovoltaic in rural areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic flow chart of a method for evaluating the spatio-temporal matching of photovoltaic output and cold storage load according to the present invention;

[0080] Figure 2 It is a schematic structural diagram of an electronic device according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0081] The following further elaborates on the specific embodiments of the present invention with reference to the drawings.

[0082] Embodiment 1

[0083] A method for evaluating the spatio-temporal matching of photovoltaic output and cold storage load provided by the present invention, as shown in Figure 1 shown, includes:

[0084] Step S110: Based on the solar illumination parameters of the target photovoltaic on the prediction day and the inherent photovoltaic parameters of the target photovoltaic, determine the daily output of the target photovoltaic on the prediction day;

[0085] Step S120: Consider the heat load of the cold storage enclosure structure, the ventilation heat load of the cold storage, the heat load of the cold storage goods, the operating heat load of the cold storage, and the running heat load of the cold storage. Based on the goods storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day, and the inherent thermal correlation parameters of the target cold storage, determine the total heat load of the target cold storage on the prediction day;

[0086] Step S130: Based on the total heat load of the target cold storage on the prediction day, perform thermoelectric conversion to obtain the total electricity load situation of the target cold storage on the prediction day;

[0087] Step S140: Perform multi-dimensional matching on the sunrise power generation situation of the target photovoltaic on the prediction day and the total electricity load situation of the target cold storage on the prediction day. Based on the matching results of each dimension, conduct matching evaluation of the target photovoltaic and the target cold storage at each time period on the prediction day to obtain the matching evaluation results of the target photovoltaic and the target cold storage at each time period on the prediction day.

[0088] In the present exemplary embodiment, the target photovoltaic and the target cold storage are objects to be matched, and the target photovoltaic and the target cold storage can be determined based on the actual situation. The target cold storage can be set in rural areas for storing cold-chain agricultural products. Cold-chain agricultural products are agricultural products that are stored using cold storage logistics to increase the storage time and sales radius of agricultural products, etc. They consume a large amount of electric energy throughout the whole process to always maintain a suitable low-temperature environment. The target photovoltaic can be a distributed photovoltaic installed in rural areas, and the target cold storage is within the power supply range of the target photovoltaic, so that when the matching degree between the two is relatively high, the cold storage load can be used as a high-quality resource for consuming distributed photovoltaic power, reducing the pressure on the municipal power grid caused by load fluctuations. The solar light parameters can include solar optical parameters and the relative position of the sun with respect to the target photovoltaic. The inherent photovoltaic parameters of the target photovoltaic include the photovoltaic panel angle information of the target photovoltaic, the photovoltaic conversion parameters of the target photovoltaic, the earth's latitude, etc. The weather conditions outside the target cold storage can include the temperature, wind speed, humidity, etc. outside the cold storage, and the inherent thermal correlation parameters of the target cold storage can include the enclosure structure parameters of the target cold storage, the heat transfer parameters of the enclosure structure, and the air thermal parameters inside the target cold storage, etc. The sunrise power generation situation of the target photovoltaic and the total electricity load situation of the target cold storage can be sunrise power generation curves and electricity load curves, such as sunrise power generation curves at 24 points or 96 points. The present invention aims at the problem that it is difficult to ensure the accuracy in evaluating the matching degree between photovoltaic power generation and cold storage load due to the volatility and uncertainty of photovoltaic power generation and cold storage load. By considering various factors such as meteorological conditions, geographical location, and time, it predicts photovoltaic power generation and cold storage load and evaluates the matching potential of the source and load, facilitating the full evaluation of the matching and consumption potential of the two.

[0089] In some exemplary embodiments, step S110 of determining the sunrise power generation situation of the target photovoltaic on the prediction day based on the solar light parameters of the target photovoltaic on the prediction day and the inherent photovoltaic parameters of the target photovoltaic includes:

[0090] Based on the solar optical parameters on the prediction day and the position information of the target photovoltaic, calculate the relative position information of the sun with respect to the target photovoltaic on the prediction day;

[0091] Based on the relative position information of the sun with respect to the target photovoltaic on the prediction day and the photovoltaic panel angle information of the target photovoltaic, calculate the solar incidence angle at the target photovoltaic on the prediction day;

[0092] Based on the solar incidence angle at the target photovoltaic on the prediction day, the solar optical parameters on the prediction day, and the photovoltaic panel angle information of the target photovoltaic, calculate the solar radiation received by the target photovoltaic on the prediction day;

[0093] Based on the solar radiation received by the target photovoltaic on the prediction day and the photoelectric conversion parameters of the target photovoltaic, calculate the predicted daily output of the target photovoltaic.

[0094] In the present exemplary embodiment, the solar optical parameters include the solar declination angle, optical depth, scattering factor, true solar time, solar hour angle, etc., and the solar optical parameters can be calculated by combining the prediction day date with empirical formulas. The position information of the target photovoltaic can include the longitude and latitude of the target photovoltaic, etc. The relative position information of the sun with respect to the target photovoltaic can include the solar altitude angle and solar azimuth angle at the target photovoltaic, etc. The photovoltaic panel angle information of the target photovoltaic includes the photovoltaic panel azimuth angle and photovoltaic panel tilt angle of the target photovoltaic, etc., and the photoelectric conversion parameters of the target photovoltaic include the photovoltaic cell panel area of the target photovoltaic and the photovoltaic cell panel conversion efficiency of the target photovoltaic, etc.

[0095] Specifically, the calculation formula for the solar optical parameters on the prediction day is as follows:

[0096]

[0097] In the formula: δ is the solar declination angle on the prediction day, k is the optical depth on the prediction day, C is the scattering factor on the prediction day, n is the date of the prediction day, calculated as 365 days in a year, n = 1 on January 1st, n = 2 on January 2nd, and so on, n = 365 on December 31st. ST is the true solar time, and H is the solar hour angle at the target photovoltaic.

[0098] The calculation formula for the solar altitude angle at the target photovoltaic on the prediction day is:

[0099] β = arcsin(cosL cosδcosH + sin L sinδ) (2)

[0100] In the formula, β is the solar altitude angle at the target photovoltaic on the prediction day, L is the geodetic latitude at the target photovoltaic, δ is the solar declination angle on the prediction day, and H is the solar hour angle at the target photovoltaic;

[0101] The calculation formula for the solar azimuth angle at the target photovoltaic on the prediction day is:

[0102]

[0103] In the formula, φS To predict the solar azimuth angle at the target photovoltaic on the prediction day;

[0104] The photovoltaic panel angle information of the target photovoltaic includes the azimuth angle and tilt angle of the photovoltaic panel of the target photovoltaic. The calculation formula for the solar incident angle at the target photovoltaic on the prediction day is:

[0105] α = arccos[cosβcos(φ S -φ C )sinξ + sinβcosξ] (4)

[0106] In the formula, α is the solar incident angle at the target photovoltaic on the prediction day, φ C is the azimuth angle of the photovoltaic panel of the target photovoltaic, and ξ is the tilt angle of the photovoltaic panel of the target photovoltaic;

[0107] The calculation formula for the solar radiation received by the target photovoltaic on the prediction day is:

[0108]

[0109] In the formula, I C is the solar radiation received by the target photovoltaic on the prediction day, I BC , I DC , I RC are the direct, diffuse, and reflected radiation amounts that can be received by the photovoltaic panel of the target photovoltaic on the prediction day respectively, I B is the direct sunlight intensity on the prediction day, I SC is the solar constant on the prediction day, k is the optical depth on the prediction day, C is the scattering factor on the prediction day, ρ p is the reflectivity of the ground where the target photovoltaic is located to solar radiation.

[0110] The photovoltaic conversion parameters of the target photovoltaic include the area of the photovoltaic cell panel of the target photovoltaic and the conversion efficiency of the photovoltaic cell panel of the target photovoltaic. The calculation formula for the daily output situation of the target photovoltaic on the prediction day is:

[0111] P PV = A p ×η×I C (6)

[0112] In the formula, P PV is the daily output situation of the target photovoltaic on the prediction day, A pLet \(S\) be the area of the photovoltaic panel of the target photovoltaic system, and \(\eta\) be the conversion efficiency of the photovoltaic panel of the target photovoltaic system. The processing prediction of the target photovoltaic system for the prediction day is carried out through the above formulas (1)-(6). The present invention comprehensively considers multiple influencing factors such as the solar declination angle, optical depth, scattering factor, solar altitude angle, solar incidence angle, and photovoltaic panel tilt angle, and uses the corresponding empirical formulas to calculate the solar radiation amount and photovoltaic output.

[0113] In an exemplary embodiment, the consideration of the cold storage enclosure heat load, cold storage ventilation heat load, cold storage cargo heat load, cold storage operation heat load, and cold storage operation heat load in step S120, based on the cargo storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day, and the inherent thermal correlation parameters of the target cold storage, to determine the total heat load of the target cold storage on the prediction day, including:

[0114] Based on the weather conditions outside the target cold storage on the prediction day, the enclosure structure parameters of the target cold storage, and the enclosure structure heat transfer parameters of the target cold storage, calculate the enclosure structure heat load of the target cold storage on the prediction day;

[0115] Based on the ventilation condition of the target cold storage on the prediction day, the air thermal parameters inside the target cold storage on the prediction day, and the weather conditions outside the target cold storage on the prediction day, calculate the ventilation heat load of the target cold storage on the prediction day;

[0116] Based on the storage volume of various types of goods in the target cold storage on the prediction day and the change in the thermal parameters of various types of goods entering the target cold storage, calculate the cargo heat load of the target cold storage on the prediction day;

[0117] Based on the lighting condition inside the target cold storage on the prediction day, the door opening and closing operation condition, and the in-cold storage operation condition of the staff, calculate the operation heat load of the target cold storage on the prediction day;

[0118] Based on the operation condition of the evaporator of the target cold storage on the prediction day, determine the operation heat load of the target cold storage on the prediction day;

[0119] Based on the enclosure structure heat load, ventilation heat load, cargo heat load, operation heat load, and operation heat load of the target cold storage on the prediction day, determine the total heat load of the target cold storage on the prediction day.

[0120] In the present exemplary embodiment, the inherent thermal correlation parameters of the target cold storage include the enclosure structure parameters of the target cold storage (such as the internal volume, the surface area of the enclosure structure, the thickness of the storage body, etc.), the heat transfer parameters of the enclosure structure (such as the heat transfer coefficient of the wall), and the air thermal parameters inside the target cold storage (such as the air thermal conductivity, the density of the air inside the storage, the temperature inside the storage); the cargo storage plan of the target cold storage includes the types of cargo stored in the target cold storage and the corresponding storage quantities, the cargo density, the ventilation conditions (such as the ventilation rate, the duration, etc.), the lighting conditions (the number of electric lights inside the storage, the power, the lighting duration, etc.), the door opening and closing operation conditions (the number of door openings and closings, the duration, etc.), the operation conditions of the staff inside the storage (the number of staff, the operation duration, etc.), and the operation conditions of the evaporator (the number of evaporator fan motors, the shaft power of the fan motors, the efficiency of the fan motors, etc.). It is also possible to obtain parameters such as the specific enthalpy before and after ventilation, the specific enthalpy before and after cargo cooling, and the respiratory heat flow before and after cargo cooling on the prediction day based on the cold storage plan. By calculating the corresponding heat loads (such as the enclosure structure heat load, the ventilation heat load, the cargo heat load, the operation heat load, and the operation heat load, etc.) according to the corresponding parameters, the total heat load of the target cold storage on the prediction day can be determined by summing up various heat loads of the target cold storage on the prediction day, such as the enclosure structure heat load, the ventilation heat load, the cargo heat load, the operation heat load, and the operation heat load.

[0121] Exemplarily, the calculation formula for the total heat load of the target cold storage on the prediction day is as follows:

[0122] Q T =Q w +Q v +Q p +Q m +Q e (7)

[0123] In the formula, Q T is the total heat load of the target cold storage on the prediction day, Q w is the enclosure structure heat load of the target cold storage on the prediction day, Q v is the ventilation heat load of the target cold storage on the prediction day, Q p is the cargo heat load of the target cold storage on the prediction day, Q m is the operation heat load of the target cold storage on the prediction day, Q e is the operation heat load of the target cold storage on the prediction day;

[0124]

[0125] In the formula, U is the total heat transfer coefficient of the enclosure structure of the target cold storage, A c is the surface area of the enclosure structure of the target cold storage, T a is the temperature outside the target cold storage on the prediction day, T is is the temperature inside the target cold storage on the prediction day, h 0For predicting the heat transfer coefficient h of the air outside the target cold storage on the prediction day i For predicting the heat transfer coefficient k of the air on the inner surface of the target cold storage on the prediction day w For the thermal conductivity of the wall of the enclosure structure of the target cold storage, Δω is the thickness of the wall of the enclosure structure of the target cold storage, and ν is the wind speed outside the target cold storage on the prediction day;

[0126]

[0127] In the formula, n t Is the daily ventilation and air change rate of the target cold storage on the prediction day, V n Is the net volume inside the target cold storage, Δh air Is the enthalpy change of the air entering the target cold storage on the prediction day, ρ air Is the air density inside the target cold storage;

[0128]

[0129] In the formula, m j,p Is the daily incoming quantity of the j-th kind of goods on the prediction day, h j,1 , h j,2 Are respectively the specific enthalpy of the initial temperature when the j-th kind of goods enters the target cold storage on the prediction day and the specific enthalpy at the end of temperature reduction in the storage, q j,1 , q j,2 Are respectively the respiratory heat per unit mass of the j-th kind of goods at the initial cooling temperature and the respiratory heat per unit mass at the end temperature on the prediction day, t j,1 , t j,2 Are respectively the temperature when the packaging material corresponding to the j-th kind of goods enters the target cold storage and the temperature at the end of temperature reduction on the prediction day, m j,b , c j,b Are respectively the mass and specific heat capacity of the outer packaging corresponding to the j-th kind of goods on the prediction day, τ j Is the cooling and processing time of the j-th kind of goods;

[0130]

[0131] In the formula, Q d Is the heat flow of floor lighting per unit area of the target cold storage, A d Is the floor area of the target cold storage, n k Is the number of cold storage doors of the target cold storage, n k ' Is the number of times the cold storage door of the target cold storage is opened on the prediction day, h w , h n Are respectively the specific enthalpies of the indoor and outdoor air of the target cold storage on the prediction day, M is the air curtain correction coefficient of the target cold storage, and its value can be taken as 0.5. When the air curtain is not set on the prediction day, M takes the value of 1; ρ n Is the density of the goods stored in the target cold storage, N is the number of operators in the target cold storage on the prediction day, Q ais the average heat load released per person per unit time, t s is the working time of the operator;

[0132] Q e = N me S / μ me (12)

[0133] In the formula, N me is the number of evaporator fan motors of the target cold storage, S is the shaft power of the evaporator fan motors of the target cold storage, μ me is the efficiency of the evaporator fan motors of the target cold storage. Combining the obtained parameter information such as the type of goods, storage temperature, storage scale, etc. with the cold storage heat load calculation formula to obtain the cold storage heat load under different storage conditions, that is, predicting the heat load of the target cold storage on the prediction day through the above formulas (7)-(11). The present invention comprehensively considers multiple influencing factors such as the temperature inside and outside the cold storage, the surface area of the enclosure structure, the heat transfer coefficient, the thermal conductivity, the thickness of the cold storage body, the wind speed, etc., and uses the corresponding empirical formulas to calculate the predicted cold storage heat load.

[0134] In an exemplary embodiment, the obtaining the total power consumption load of the target cold storage on the prediction day based on the thermoelectric conversion of the total heat load of the target cold storage on the prediction day includes:

[0135] Performing thermoelectric conversion on the total heat load of the target cold storage on the prediction day based on the condensation coefficient of the target cold storage on the prediction day obtained by fitting to obtain the power consumption of the compressor of the target cold storage on the prediction day;

[0136] Based on the lighting power consumption, condenser motor power, evaporator motor power and compressor power consumption of the target cold storage on the prediction day, determining the total power consumption load situation of the target cold storage on the prediction day.

[0137] In this exemplary embodiment, the condensation coefficient of the target cold storage can be obtained through the evaporation temperature and condensation temperature of the refrigerant and the refrigerant fitting equation. Specifically, it is shown as follows:

[0138]

[0139] In the formula, COP is the condensation coefficient of the target cold storage, T e , T c are respectively the evaporation temperature and condensation temperature of the refrigerant of the target cold storage, α is the empirical constant of the refrigerant of the target cold storage, n m is the empirical constant of the refrigeration system of the target cold storage, μ comp is the compressor efficiency of the target cold storage, and χ is the fitting parameter of the refrigerant of the target cold storage. Calculating the power consumption of the compressor of the target cold storage based on the condensation coefficient and the total heat load of the target cold storage on the prediction day, specifically as follows:

[0140]

[0141] In the formula, P comp is the power consumption of the compressor on the prediction day.

[0142] By obtaining the motor power of the condenser and evaporator of the target cold storage on the prediction day, the lighting power consumption, etc., and combining with the compressor power consumption, the total power load of the target cold storage on the prediction day is obtained. Specifically as follows:

[0143] P load = P comp + P l + P mc + P me (15)

[0144] In the formula, P load is the total power load of the target cold storage on the prediction day, P l is the lighting power consumption of the target cold storage on the prediction day, P mc is the motor power of the condenser of the target cold storage on the prediction day, P mc = s mc / μ mc where s mc , μ mc are the shaft power and efficiency of the condenser respectively, P me is the motor power of the evaporator of the target cold storage on the prediction day, P me = s me / μ me where s me , μ me are the shaft power and efficiency of the evaporator respectively.

[0145] Based on various heat loads, relevant data are combined with relevant calculation formulas of the refrigeration system to obtain the total power load of the refrigeration system. The parameter information includes evaporation and condensation temperature, refrigerant fitting equation, empirical constant, condenser and evaporator efficiency, etc. The thermoelectric conversion of the target cold storage is realized through formulas (13)-(15).

[0146] In an exemplary embodiment, the multi-dimensional matching of the sunrise power generation situation of the target photovoltaic on the prediction day and the total power load situation of the target cold storage on the prediction day in step S140 includes:

[0147] Based on the correlation analysis of the sunrise power generation situation of the target photovoltaic on the prediction day and the total power load situation of the target cold storage on the prediction day for each time period, the fluctuation matching index in the fluctuation dimension for each time period on the prediction day is obtained;

[0148] Based on the ratio of the sunrise power generation situation of the target photovoltaic to the total power load situation at each prediction moment on the prediction day, the power matching index in the power dimension for each time period on the prediction day is determined.

[0149] In this exemplary embodiment, the multi-dimensional matching may include the matching of the volatility dimension and the matching of the power quantity dimension, and may also include other dimensions such as the accommodation dimension. It can be matched by time periods or for the whole day. For the volatility dimension, Pearson correlation analysis can be used to determine the corresponding volatility matching index, and for the power quantity dimension, the corresponding power quantity matching index can be directly determined by the ratio of two data. The specific volatility matching index is calculated according to the following formula:

[0150]

[0151] In the formula: M wave is the volatility matching index corresponding to the current matching time period, which is used to characterize the volatility matching degree between the cold storage load curve and the photovoltaic output curve during the photovoltaic output period, and the value range is [-1, 1], P load,ave and P pv,ave are respectively the average daily output of the target cold storage load and the target photovoltaic, t 1 and t 2 are respectively the start time and the end time of the matching time period.

[0152] The calculation formula of the power quantity matching index is as follows:

[0153]

[0154] In the formula, W e is the power quantity matching index corresponding to the current matching time period, which is used to characterize the matching degree between the total photovoltaic power generation and the total cold storage power consumption during the photovoltaic output period, and the value range is [0, +∞). The closer it is to 1, the higher the matching degree between the total photovoltaic power generation and the total load power consumption.

[0155] In an exemplary embodiment, the matching evaluation of the target photovoltaic and the target cold storage in each time period of the prediction day based on the matching results of each dimension to obtain the matching evaluation results of the target photovoltaic and the target cold storage in each time period of the prediction day includes:

[0156] Performing a matching strength analysis on the matching index of each dimension in each time period of the prediction day based on the membership function corresponding to the matching index of each dimension to obtain the true value corresponding to the matching index of each dimension in each time period of the prediction day;

[0157] Performing weighted fusion on the true values corresponding to the matching indexes of each dimension in each time period of the prediction day based on the weight corresponding to each matching index in the prediction day to obtain the matching evaluation results of the target photovoltaic and the target cold storage in each time period of the prediction day.

[0158] In this exemplary embodiment, the matching evaluation of the source-load output curves can be carried out for the entire prediction day or for different time periods of the prediction day. Through the matching evaluation of different time periods, more precise control of the cold storage function can be achieved. The weight corresponding to each matching index on the prediction day is determined based on the multi-dimensional matching results of several days adjacent to the prediction day, that is, the weights corresponding to the matching indexes of each dimension can be determined through the multi-dimensional matching results (including the matching indexes of each dimension) of multiple prediction days. Specifically, in the evaluation and matching process, a corresponding membership function can be set for each matching dimension, so as to obtain the true value of the matching index of the corresponding dimension. The true value is weighted and summed using the corresponding weights to obtain the final comprehensive matching score, that is, the matching evaluation result of the target photovoltaic and the target cold storage.

[0159] Exemplarily, the calculation of the true values of the corresponding indexes for the volatility dimension and the electricity quantity dimension is as follows: For the volatility dimension, according to the Pearson coefficient correlation degree interval, let the value of the volatility matching index be x 1 , and the following membership function corresponding to the source-load curve correlation is set:

[0160]

[0161] In the formula, μ(x 1 ) is the true value corresponding to the volatility matching index x 1 . When μ(x 1 ) > 0.5, the source-load curves have at least medium correlation, that is, the fluctuation trends are basically the same. When μ(x 1 ) < 0.25, the source-load curves are negatively correlated, that is, the fluctuation trends are opposite, and the load valley period approximately coincides with the photovoltaic output peak period.

[0162] Let the value of the electricity quantity matching degree index be x 2 , and the following membership function corresponding to the source-load curve correlation is set:

[0163]

[0164] In the formula, μ(x 2 ) is the true value corresponding to the electricity quantity matching index x 2 .

[0165] Through the membership functions of the above-mentioned dimensions, the true values corresponding to each index are obtained, and then weights are assigned to the true values corresponding to each index to obtain the comprehensive source-load matching score (that is, the matching evaluation result of the target photovoltaic and the target cold storage in the current matching period). According to the score result, the matching degree between the photovoltaic output and the cold storage load is evaluated. The matching evaluation result of the target photovoltaic and the target cold storage is as follows:

[0166] y = w 1 x' 1 + w2 x' 2 +...+w m x' m =W T X' (20)

[0167] In the formula, y is the comprehensive score of source-load matching, x' m is the true value corresponding to the m-th matching index, w m is the weight corresponding to the m-th matching index, X' = (x' 1 , x' 2 , x' 3 ,..., x' m ) T is the matching index vector, W = (w 1 , w 2 , w 3 ,..., w m ) T is the matching index weight vector, and the superscript T represents the matrix transpose.

[0168] Exemplarily, before obtaining the matching evaluation results of the target photovoltaic and the target cold storage at each time period of the prediction day based on the matching results of each dimension, multi-dimensional matching results within the future target time range are obtained; taking the multi-dimensional matching results of each day within the future target time range as an evaluation object, and taking the true value of the matching index of one dimension of each day as an evaluation index corresponding to the evaluation object, based on the multi-dimensional matching results within the future target time range, the weight corresponding to each matching index of the prediction day is determined using the method of differentiating grades;

[0169] In this exemplary embodiment, the future target time range includes the prediction day and several days adjacent to the prediction day, and the weight corresponding to each dimension matching index can be comprehensively determined through the prediction results within the future target time range. Specifically, the method of differentiating grades based on the "gap-driven" principle is used to assign weights to the matching indexes of each matching dimension (such as the fluctuation matching index and the power matching index). Specifically as follows, let:

[0170] Y = (y 1 , y 2 , y 3 ,..., y r ) T ,

[0171] Y T Y = (W T A T )AW = W T HW,

[0172]

[0173] In the formula, Y is the comprehensive source-load matching score vector within the future target time range, and y r is the comprehensive source-load matching score on the r-th prediction day, A is the matching index matrix, H is the intermediate matrix, and x' rm is the true value corresponding to the m-th matching index on the r-th prediction day. To meet the basic requirements of the index weights, it is specified that W T W = 1, then the weight solution problem is transformed into solving the following non-linear programming problem:

[0174]

[0175] Using the matlab software, W can be calculated, that is, the weights corresponding to the matching indexes of each dimension are obtained. Based on the source-load matching index model, the present invention calculates the matching degree between the photovoltaic output and the cold storage load, uses the rank-difference method to assign weights to the indexes and calculate the comprehensive score, and realizes the evaluation of the matching degree between the photovoltaic output and the cold storage load.

[0176] In an exemplary embodiment, after performing the matching evaluation of the target photovoltaic and the target cold storage at each time period on the prediction day based on the matching results of each dimension, and obtaining the matching evaluation results of the target photovoltaic and the target cold storage at each time period on the prediction day, it further includes:

[0177] If the matching evaluation results of the target photovoltaic and the target cold storage at each time period on the prediction day are less than the preset threshold, adjust the operation mode of the target cold storage.

[0178] In this exemplary embodiment, the matching evaluation result of the target photovoltaic and the target cold storage can be the comprehensive source-load matching score y. Based on this comprehensive score y, the matching degree between the photovoltaic output and the cold storage load is evaluated. The preset threshold can be set to 0.75 - 0.85. For example, when the comprehensive score y is greater than 0.8, it indicates that the matching degree between the two is good; when the comprehensive score y is less than or equal to 0.8, it indicates that the matching degree between the two is not good. According to the calculation results of the source-load matching index, the operation mode of the cold storage can be adjusted, such as adjusting the incoming goods quantity, changing the photovoltaic / mains power usage method, etc.

[0179] In view of the rapid development of the storage and preservation technology of fresh agricultural products in China and the growth of market demand, the number of fresh agricultural products transported by cold storage logistics has gradually increased, and the cold storage logistics of fresh agricultural products has developed rapidly. There is an abundance of agricultural products in rural areas. In order to increase the storage time and sales radius, etc., it is necessary to use cold storage logistics to consume a large amount of electric energy in all links to always maintain a suitable low-temperature environment. At the same time, there is a certain potential for spatio-temporal matching between the cold storage load and distributed photovoltaic power. Therefore, the cold storage load can be used as a high-quality resource for consuming distributed photovoltaic power. Also, considering the influence of uncontrollable factors such as meteorological conditions and market prices, the output of photovoltaic power and the cold storage load often show uncertainty, making it difficult to accurately characterize their spatio-temporal correlation and fully evaluate the matching degree between the agricultural cold storage load and distributed photovoltaic power at different spatio-temporal scales.

[0180] In view of the above actual situation, the present invention proposes a spatio-temporal matching evaluation method for photovoltaic power output and cold storage load. By combining the obtained parameter information such as the earth's latitude and solar position with the light intensity calculation formula, the solar radiation amount at different times and locations is obtained; based on the solar radiation amount, the photovoltaic power output at different times and locations is obtained by combining the photovoltaic power output calculation formula; by combining the obtained parameter information such as the type of goods, storage temperature, and storage scale with the cold storage heat load calculation formula, the cold storage heat load under different storage conditions is obtained; based on the cold storage heat load, the parameter information is combined with the relevant calculation formula of the refrigeration system to obtain the total electricity load of the cold storage; based on the solar radiation amount and the total electricity load, the obtained outdoor parameter information is combined with the cold storage load prediction method to predict the electricity load of the cold storage. Finally, based on the source-load matching index, the matching degree between the photovoltaic power output and the cold storage load is calculated, and the index is weighted by the rank difference method and the comprehensive score is calculated to achieve an accurate evaluation of the matching degree between the photovoltaic power output and the cold storage load.

[0181] Embodiment 2

[0182] Based on the same inventive concept, the present invention also provides a spatio-temporal matching evaluation system for photovoltaic power output and cold storage load. The system includes:

[0183] A photovoltaic power output prediction module, configured to determine the daily output situation of the target photovoltaic power based on the solar illumination parameters of the target photovoltaic on the prediction day and the inherent photovoltaic parameters of the target photovoltaic;

[0184] A cold storage load prediction module, configured to consider the cold storage enclosure heat load, cold storage ventilation heat load, cold storage cargo heat load, cold storage operation heat load, and cold storage operation heat load, and based on the cargo storage plan of the target cold storage on the prediction day, the weather conditions outside the target cold storage on the prediction day, and the inherent heat correlation parameters of the target cold storage, determine the total heat load of the target cold storage on the prediction day; based on the thermoelectric conversion of the total heat load of the target cold storage on the prediction day, obtain the total electricity load situation of the target cold storage on the prediction day;

[0185] A matching evaluation module, which is used to perform multi-dimensional matching on the predicted daily output of the target photovoltaic and the total electricity load of the target cold storage on the predicted day, and perform matching evaluation on the target photovoltaic and the target cold storage at each time period on the predicted day based on the matching results of each dimension, so as to obtain the matching evaluation results of the target photovoltaic and the target cold storage at each time period on the predicted day;

[0186] Wherein, the target cold storage is within the power supply range of the target photovoltaic.

[0187] In a possible implementation manner, the solar illumination parameters of the target photovoltaic on the predicted day include the relative position of the sun relative to the target photovoltaic, and the inherent photovoltaic parameters of the target photovoltaic include the photovoltaic panel angle information of the target photovoltaic and the photovoltaic conversion parameters of the target photovoltaic. The photovoltaic output prediction module includes:

[0188] A relative position calculation sub-module, which is used to calculate the relative position information of the sun relative to the target photovoltaic on the predicted day based on the solar optical parameters on the predicted day and the position information of the target photovoltaic;

[0189] An incident angle calculation sub-module, which is used to calculate the solar incident angle at the target photovoltaic on the predicted day based on the relative position information of the sun relative to the target photovoltaic on the predicted day and the photovoltaic panel angle information of the target photovoltaic;

[0190] A radiation amount calculation sub-module, which is used to calculate the solar radiation amount received by the target photovoltaic on the predicted day based on the solar incident angle at the target photovoltaic on the predicted day, the solar optical parameters on the predicted day and the photovoltaic panel angle information of the target photovoltaic;

[0191] An output prediction sub-module, which is used to calculate the predicted daily output of the target photovoltaic based on the solar radiation amount received by the target photovoltaic on the predicted day and the photovoltaic conversion parameters of the target photovoltaic.

[0192] In a possible implementation manner, the solar optical parameters on the predicted day include the solar declination angle on the predicted day, the optical depth on the predicted day, and the scattering factor on the predicted day. The relative position information of the sun relative to the target photovoltaic on the predicted day includes the solar altitude angle and the solar azimuth angle at the target photovoltaic on the predicted day. The calculation formula for the solar altitude angle at the target photovoltaic on the predicted day is:

[0193] β = arcsin(cosL cosδcosH + sinL sinδ);

[0194] In the formula, β is the solar altitude angle at the target photovoltaic on the predicted day, L is the geodetic latitude at the target photovoltaic, δ is the solar declination angle on the predicted day, and H is the solar hour angle at the target photovoltaic;

[0195] The calculation formula for the solar azimuth angle at the target photovoltaic on the predicted day is:

[0196]

[0197] Wherein, φ S is the solar azimuth angle at the target photovoltaic on the prediction day;

[0198] The photovoltaic panel angle information of the target photovoltaic includes the azimuth angle and tilt angle of the photovoltaic panel of the target photovoltaic. The calculation formula for the solar incident angle at the target photovoltaic on the prediction day is:

[0199] α = arccos[cosβcos(φ S - φ C )sinξ + sinβcosξ];

[0200] Wherein, α is the solar incident angle at the target photovoltaic on the prediction day, φ C is the azimuth angle of the photovoltaic panel of the target photovoltaic, and ξ is the tilt angle of the photovoltaic panel of the target photovoltaic;

[0201] The calculation formula for the solar radiation received by the target photovoltaic on the prediction day is:

[0202] I C = I BC + I DC + I RC ;

[0203] I BC = I B cosα;

[0204]

[0205] Wherein, I C is the solar radiation received by the target photovoltaic on the prediction day, I BC , I DC , I RC are respectively the direct, scattered, and reflected radiation amounts that can be received by the photovoltaic panel of the target photovoltaic on the prediction day, I B is the direct light intensity on the prediction day, I SC is the solar constant on the prediction day, k is the optical depth on the prediction day, C is the scattering factor on the prediction day, and ρ p is the reflectivity of the ground where the target photovoltaic is located to solar radiation;

[0206] The photovoltaic conversion parameters of the target photovoltaic include the area of the photovoltaic cell panel of the target photovoltaic and the conversion efficiency of the photovoltaic cell panel of the target photovoltaic. The calculation formula for the sunrise power situation of the target photovoltaic on the prediction day is:

[0207] P PV = A p × η × I C ;

[0208] Wherein, P PV is the predicted daily target PV output, A p is the area of the PV panels of the target PV, and η is the conversion efficiency of the PV panels of the target PV.

[0209] In a possible implementation, the inherent thermal correlation parameters of the target cold storage include the enclosure structure parameters of the target cold storage, the heat transfer parameters of the enclosure structure, and the air thermal parameters inside the target cold storage; the cargo storage plan of the target cold storage includes the types of goods stored in the target cold storage and the corresponding storage quantities, ventilation conditions, lighting conditions, door opening and closing operations, in-store operations of the staff, and evaporator operation conditions; the cold storage load prediction module includes a cold storage heat load prediction sub-module, and the cold storage heat load prediction sub-module is used for:

[0210] Based on the weather conditions outside the target cold storage on the prediction day, the enclosure structure parameters of the target cold storage, and the heat transfer parameters of the enclosure structure of the target cold storage, calculate the enclosure structure heat load of the target cold storage on the prediction day;

[0211] Based on the ventilation conditions of the target cold storage on the prediction day, the air thermal parameters inside the target cold storage on the prediction day, and the weather conditions outside the target cold storage on the prediction day, calculate the ventilation heat load of the target cold storage on the prediction day;

[0212] Based on the storage quantities of various goods in the target cold storage on the prediction day and the changes in the thermal parameters of various goods entering the target cold storage, calculate the cargo heat load of the target cold storage on the prediction day;

[0213] Based on the lighting conditions, door opening and closing operations, and in-store operations of the staff inside the target cold storage on the prediction day, calculate the operation heat load of the target cold storage on the prediction day;

[0214] Based on the evaporator operation conditions of the target cold storage on the prediction day, determine the operation heat load of the target cold storage on the prediction day;

[0215] Based on the enclosure structure heat load, ventilation heat load, cargo heat load, operation heat load, and operation heat load of the target cold storage on the prediction day, determine the total heat load of the target cold storage on the prediction day.

[0216] In a possible implementation, the calculation formula for the total heat load of the target cold storage on the prediction day is as follows:

[0217] Q T = Q w + Q v + Q p + Q m + Q e ;

[0218] Wherein, Q TFor predicting the total heat load, Q, of the target cold storage on the prediction day w For the heat load of the enclosure structure of the target cold storage on the prediction day, Q v For the ventilation heat load of the target cold storage on the prediction day, Q p For the heat load of the goods in the target cold storage on the prediction day, Q m For the operating heat load of the target cold storage on the prediction day, Q e For the running heat load of the target cold storage on the prediction day;

[0219] Q w = U × A c × (T a - T is )

[0220]

[0221] h 0 = 5.62 + 3.9ν;

[0222] In the formula, U is the total heat transfer coefficient of the enclosure structure of the target cold storage, A c is the surface area of the enclosure structure of the target cold storage, T a is the outdoor temperature of the target cold storage on the prediction day, T is is the indoor temperature of the target cold storage on the prediction day, h 0 is the outdoor air heat transfer coefficient of the target cold storage on the prediction day, h i is the indoor surface air heat transfer coefficient of the target cold storage on the prediction day, k w is the wall thermal conductivity of the enclosure structure of the target cold storage, Δω is the wall thickness of the enclosure structure of the target cold storage, ν is the outdoor wind speed of the target cold storage on the prediction day;

[0223]

[0224] In the formula, n t is the daily ventilation and air change rate of the target cold storage on the prediction day, V n is the net volume inside the target cold storage, Δh air is the enthalpy change of the air entering the target cold storage on the prediction day, ρ air is the air density inside the target cold storage;

[0225]

[0226] In the formula, m j,p is the daily incoming quantity of the j-th kind of goods on the prediction day, h j,1 、h j,2 are respectively the specific enthalpy of the initial temperature when the j-th kind of goods enters the target cold storage and the specific enthalpy at the end of temperature reduction in the storage on the prediction day, q j,1 、q j,2are the heat of respiration per unit mass of the j-th type of goods at the initial cooling temperature and the heat of respiration per unit mass at the termination temperature on the prediction day, respectively, and t j,1 and t j,2 are the temperatures of the corresponding packaging material of the j-th type of goods when entering the target cold storage and at the termination of temperature reduction on the prediction day, respectively, m j,b and c j,b are the mass and specific heat capacity of the outer packaging of the j-th type of goods on the prediction day, respectively, and τ j is the cooling processing time of the j-th type of goods;

[0227]

[0228] In the formula, Q d is the heat flux of floor lighting per unit area of the target cold storage, A d is the floor area of the target cold storage, n k is the number of cold storage doors of the target cold storage, n k ' is the number of times the cold storage door of the target cold storage is opened per day on the prediction day, h w and h n are the specific enthalpies of indoor and outdoor air in the target cold storage on the prediction day, respectively. M is the air curtain correction coefficient of the target cold storage, and its value can be taken as 0.5. When the air curtain is not set on the prediction day, M takes the value of 1, ρ n is the density of the goods stored in the target cold storage, N is the number of operators in the target cold storage on the prediction day, and Q a is the average heat load released per person per unit time, and t s is the working time of the operator;

[0229] Q e = N me S / μ me ;

[0230] In the formula, N me is the number of evaporator fan motors in the target cold storage, S is the shaft power of the evaporator fan motor in the target cold storage, and μ me is the efficiency of the evaporator fan motor in the target cold storage.

[0231] In a possible implementation manner, the cold storage load prediction module further includes a thermoelectric conversion sub-module, and the thermoelectric conversion sub-module is used for:

[0232] Performing thermoelectric conversion on the total heat load of the target cold storage on the prediction day based on the condensation coefficient of the target cold storage on the prediction day obtained by fitting to obtain the power consumption of the compressor of the target cold storage on the prediction day;

[0233] Determining the total power load situation of the target cold storage on the prediction day based on the lighting power consumption, condenser motor power, evaporator motor power, and compressor power consumption of the target cold storage on the prediction day.

[0234] In a possible implementation manner, the matching evaluation module includes a matching sub-module, and the matching sub-module is configured to:

[0235] Based on the correlation analysis of the predicted daily output of the target photovoltaic and the total electricity load of the target cold storage in each time period, obtain the fluctuation matching index of each time period on the prediction day in the volatility dimension;

[0236] Based on the ratio of the predicted daily output of the target photovoltaic to the total electricity load at each predicted moment on the prediction day, perform matching in the electricity dimension, and determine the electricity matching index of each time period on the prediction day in the electricity dimension.

[0237] In a possible implementation manner, the matching evaluation module further includes an evaluation sub-module, and the evaluation sub-module is configured to:

[0238] Based on the membership function corresponding to the matching index of each dimension, perform matching strength analysis on the matching index of each dimension at each time period on the prediction day, and obtain the true value corresponding to the matching index of each dimension at each time period on the prediction day;

[0239] Based on the weight corresponding to each matching index on the prediction day, perform weighted fusion on the true values corresponding to the matching indexes of each dimension at each time period on the prediction day, and obtain the matching evaluation result of the target photovoltaic and the target cold storage at each time period on the prediction day;

[0240] Wherein, the weight corresponding to each matching index on the prediction day is determined based on the multi-dimensional matching results of several days adjacent to the prediction day.

[0241] In a possible implementation manner, the evaluation sub-module is further configured to:

[0242] Obtain multi-dimensional matching results within a future target time range;

[0243] Taking the multi-dimensional matching results of each day within the future target time range as an evaluation object, taking the true value of the matching index of one dimension of each day as an evaluation index corresponding to the evaluation object, and based on the multi-dimensional matching results within the future target time range, use the method of pulling apart grades to determine the weight corresponding to each matching index on the prediction day;

[0244] Wherein, the future target time range includes the prediction day and several days adjacent to the prediction day.

[0245] In a possible implementation manner, the system further includes:

[0246] An operation regulation module, configured to adjust the operation mode of the target cold storage if the matching evaluation result of the target photovoltaic and the target cold storage at each time period on the prediction day is less than a preset threshold.

[0247] Embodiment 3

[0248] As Figure 2 shown, the present invention also provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected through a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0249] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for spatio-temporal matching evaluation of photovoltaic output and cold storage load in the above embodiment.

[0250] Embodiment 4

[0251] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device and is used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. By loading and executing one or more instructions stored in the storage medium by the processor, the steps of a method for spatio-temporal matching evaluation of photovoltaic output and cold storage load in the above embodiment can be implemented.

[0252] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0253] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0254] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realize the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0255] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0256] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the scope of the protection of the claims pending for approval of the application.

Claims

1. A method for evaluating the spatiotemporal matching of photovoltaic output and cold storage load, characterized in that: include: Determining the daily power generation of the target photovoltaic on the forecast day based on the solar illumination parameters of the target photovoltaic on the forecast day and the inherent photovoltaic parameters of the target photovoltaic; Considering the heat load of the cold storage enclosure structure, the heat load of the cold storage ventilation, the heat load of the cold storage cargo, the heat load of the cold storage operation and the heat load of the cold storage operation, the total heat load of the target cold storage on the forecast day is determined based on the cargo storage plan of the target cold storage on the forecast day, the weather conditions outside the target cold storage on the forecast day and the inherent heat-related parameters of the target cold storage; Based on the thermoelectric conversion of the total heat load of the target cold storage on the forecast day, the total power load of the target cold storage on the forecast day is obtained; Perform multi-dimensional matching on the daily power output of the target photovoltaic system on the forecast day and the total power load of the target cold storage on the forecast day, and perform matching evaluation on the target photovoltaic system and the target cold storage in each time period of the forecast day based on the matching results of each dimension, and obtain the matching evaluation results of the target photovoltaic system and the target cold storage in each time period of the forecast day; Among them, the target cold storage is within the power supply range of the target photovoltaic.

2. The method according to claim 1, characterized in that The solar illumination parameters of the target photovoltaic on the predicted day include the relative position of the sun with respect to the target photovoltaic, the inherent photoelectric parameters of the target photovoltaic include the photovoltaic panel angle information of the target photovoltaic and the photoelectric conversion parameters of the target photovoltaic, and the determining of the daily power generation situation of the target photovoltaic on the predicted day based on the solar illumination parameters of the target photovoltaic on the predicted day and the inherent photoelectric parameters of the target photovoltaic includes: Based on the solar optical parameters of the forecast day and the position information of the target photovoltaic, the relative position information of the sun with respect to the target photovoltaic on the forecast day is calculated; Calculating the solar incident angle at the target photovoltaic on the forecast day based on the relative position information of the sun with respect to the target photovoltaic on the forecast day and the photovoltaic panel angle information of the target photovoltaic; Calculate the solar radiation received by the target photovoltaic on the forecast day based on the solar incident angle at the target photovoltaic on the forecast day, the solar optical parameters on the forecast day and the photovoltaic panel angle information of the target photovoltaic; Based on the solar radiation received by the target photovoltaic on the forecast day and the photoelectric conversion parameters of the target photovoltaic, the daily power generation situation of the target photovoltaic on the forecast day is calculated.

3. The method according to claim 2, characterized in that The solar optical parameters of the forecast day include the solar declination angle of the forecast day, the optical depth of the forecast day and the scattering factor of the forecast day. The relative position information of the sun relative to the target photovoltaic on the forecast day includes the solar altitude angle and the solar azimuth angle at the target photovoltaic on the forecast day. The calculation formula of the solar altitude angle at the target photovoltaic on the forecast day is: β=arcsin(cosL cosδcosH+sin L sinδ); Where, β is the solar altitude angle at the target photovoltaic on the forecast day, L is the geodetic latitude at the target photovoltaic, δ is the solar declination angle on the forecast day, and H is the solar hour angle at the target photovoltaic; The calculation formula for the solar azimuth angle at the target photovoltaic on the predicted day is: In the formula, φ S is the solar azimuth at the target photovoltaic on the predicted day; The photovoltaic panel angle information of the target photovoltaic includes the photovoltaic panel azimuth and photovoltaic panel tilt angle of the target photovoltaic. The calculation formula of the solar incident angle at the target photovoltaic on the predicted day is: α=arccos[cosβcos(φ S -f C )sinξ+sinβcosξ]; Where α is the solar incident angle at the target photovoltaic on the predicted day, φ C is the azimuth angle of the photovoltaic panel of the target photovoltaic, ξ is the tilt angle of the photovoltaic panel of the target photovoltaic; The calculation formula for the solar radiation received by the target photovoltaic on the predicted day is: In the formula, I C To predict the solar radiation received by the target photovoltaic system on a daily basis, I BC ,I DC ,I RC are the direct, scattered and reflected radiation that the photovoltaic panels of the target photovoltaic on the predicted day can receive, I B is the predicted direct sunlight intensity of the day, I SC is the solar constant of the prediction day, k is the optical depth of the prediction day, C is the scattering factor of the prediction day, ρ p is the reflectivity of the target photovoltaic site facing solar radiation; The target photovoltaic photoelectric conversion parameters include the target photovoltaic photovoltaic panel area and the target photovoltaic photovoltaic panel conversion efficiency. The calculation formula for the daily output of the target photovoltaic on the predicted day is: P PV =A p ×η×I C ; Where P PV To predict the daily output of the target photovoltaic power generation, A p is the photovoltaic panel area of ​​the target photovoltaic, and η is the photovoltaic panel conversion efficiency of the target photovoltaic.

4. The method according to claim 1, characterized in that: The inherent heat-related parameters of the target cold storage include the enclosure structure parameters of the target cold storage, the enclosure structure heat transfer parameters and the air heat parameters in the target cold storage; the cargo storage plan of the target cold storage includes the types of cargo stored in the target cold storage and the corresponding storage volume, ventilation conditions, lighting conditions, door opening and closing operation conditions, staff's in-store operation conditions and evaporator operation conditions; the total heat load of the target cold storage on the forecast day is determined based on the cargo storage plan of the target cold storage on the forecast day, the weather conditions outside the target cold storage on the forecast day and the inherent heat-related parameters of the target cold storage, including: Based on the weather conditions outside the target cold storage on the forecast day, the enclosure structure parameters of the target cold storage and the enclosure structure heat transfer parameters of the target cold storage, the heat load of the enclosure structure of the target cold storage on the forecast day is calculated; Based on the ventilation conditions of the target cold storage on the forecast day, the air thermal parameters in the target cold storage on the forecast day, and the weather conditions outside the target cold storage on the forecast day, the ventilation heat load of the target cold storage on the forecast day is calculated; Based on the storage volume of various goods in the target cold storage on the forecast day and the changes in thermal parameters of various goods entering the target cold storage, the cargo heat load of the target cold storage on the forecast day is calculated; Based on the lighting conditions, door opening and closing operations and staff operations in the target cold storage on the forecast day, the operating heat load of the target cold storage on the forecast day is calculated; Determine the operating heat load of the target cold storage on the forecast day based on the operating condition of the evaporator of the target cold storage on the forecast day; Based on the enclosure heat load, ventilation heat load, cargo heat load, operation heat load and running heat load of the target cold storage on the forecast day, the total heat load of the target cold storage on the forecast day is determined.

5. The method according to claim 4, characterized in that The calculation formula for the total heat load of the target cold storage on the forecast day is as follows: Q T =Q w +Q v +Q p +Q m +Q e ; In the formula, Q T is the total heat load of the target cold storage on the predicted day, Q w To predict the heat load of the enclosure structure of the target cold storage on the day, Q v To predict the ventilation heat load of the target cold storage on a daily basis, Q p To predict the heat load of the cargo in the target cold storage, Q m To predict the operating heat load of the daily target cold storage, Q e To predict the operating heat load of the daily target cold storage; Where U is the total heat transfer coefficient of the enclosure structure of the target cold storage, A c is the surface area of ​​the enclosure structure of the target cold storage, T a To predict the daily target cold storage external temperature, T is is the temperature inside the cold storage for the predicted day, h0 is the heat transfer coefficient of the air outside the cold storage for the predicted day, and h i is the predicted daily target cold storage internal surface air heat transfer coefficient, k w is the thermal conductivity of the wall of the enclosure structure of the target cold storage, Δω is the wall thickness of the enclosure structure of the target cold storage, and ν is the wind speed outside the target cold storage on the predicted day; Where n t To predict the daily ventilation times of the target cold storage, V n Net volume of target cold storage, Δh air Predict the enthalpy change of the air entering the target cold storage on the day, ρ air Air density in the target cold storage; In the formula, m j,p To predict the daily purchase volume of the jth type of goods, h j,1 、h j,2 are the specific enthalpy of the initial temperature when the j-th type of goods enters the target cold storage on the forecast day and the specific enthalpy when the temperature drops in the cold storage, q j,1 ,q j,2 are the respiration heat per unit mass of the jth cargo at the initial cooling temperature and the respiration heat per unit mass at the final cooling temperature on the forecast day, t j,1 ,t j,2 are the temperatures of the packaging materials corresponding to the jth type of goods on the forecast day when they enter the target cold storage and when the cooling is terminated, m j,b 、c j,b are the mass and specific heat capacity of the outer packaging of the jth type of cargo on the forecast day, τ j is the cooling processing time of the jth type of goods; In the formula, Q d is the floor lighting heat flux per unit area of ​​the target cold storage, A d is the floor area of ​​the target cold storage, n k is the number of cold storage doors of the target cold storage, n k ' is the predicted target number of cold storage door openings per day, h w 、h n are the specific enthalpy of indoor and outdoor air in the target cold storage on the predicted day, M is the air curtain correction coefficient of the target cold storage, ρ n is the density of refrigerated goods in the target cold storage, N is the number of operators in the target cold storage on the forecast day, Q a is the average heat load released per person per unit time, t s Working hours for operators; Q e =N me S / m me ; Where N me is the number of evaporator fan motors of the target cold storage, S is the shaft power of the evaporator fan motor of the target cold storage, μ me Evaporator fan motor efficiency of the target cold storage.

6. The method according to claim 4, characterized in that The total power load of the target cold storage on the forecast day is obtained by performing thermoelectric conversion on the total heat load of the target cold storage on the forecast day, including: Based on the condensation coefficient of the target cold storage on the forecast day obtained by fitting, the total heat load of the target cold storage on the forecast day is converted into electricity to obtain the power consumption of the compressor of the target cold storage on the forecast day; Based on the lighting power consumption, condenser motor power, evaporator motor power and compressor power consumption of the target cold storage on the forecast day, the total power load of the target cold storage on the forecast day is determined.

7. The method according to claim 1, characterized in that The multi-dimensional matching of the daily power output of the target photovoltaic system on the forecast day and the total power load of the target cold storage system on the forecast day includes: Based on the correlation analysis of the daily power output of the target photovoltaic system on the forecast day and the total power load of the target cold storage on the forecast day, the volatility matching index of each period of the forecast day in the volatility dimension is obtained; The power dimension is matched based on the ratio of the daily power output of the target photovoltaic system to the total power load at each forecast time on the forecast day, and the power matching index in the power dimension for each period of the forecast day is determined.

8. The method according to claim 7, characterized in that The matching evaluation of the target photovoltaic and the target cold storage in each time period of the forecast day is performed based on the matching result of each dimension, and the matching evaluation results of the target photovoltaic and the target cold storage in each time period of the forecast day are obtained, including: Based on the membership function corresponding to the matching index of each dimension, the matching strength analysis is performed on the matching index of each dimension in each period of the forecast day to obtain the true value corresponding to the matching index of each dimension in each period of the forecast day; Based on the corresponding weight of each matching index on the forecast day, the real value corresponding to the matching index of each dimension in each time period of the forecast day is weighted and integrated to obtain the matching evaluation result between the target photovoltaic and the target cold storage in each time period of the forecast day; The weight corresponding to each matching index of the forecast day is determined based on the multi-dimensional matching results of several days adjacent to the forecast day.

9. The method according to claim 8, characterized in that Before performing a matching evaluation of the target photovoltaic system and the target cold storage system in each time period of the forecast day based on the matching result of each dimension and obtaining the matching evaluation result of the target photovoltaic system and the target cold storage system in each time period of the forecast day, the method further includes: Obtain multi-dimensional matching results within the future target time range; Taking the daily multi-dimensional matching results within the future target time range as an evaluation object, taking the real value corresponding to the matching index of one dimension of each day as an evaluation index of the corresponding evaluation object, and based on the multi-dimensional matching results within the future target time range, using the spread-out method to determine the corresponding weight of each matching index on the forecast day; The future target time range includes the forecast date and several days adjacent to the forecast date.

10. The method according to any one of claims 1 to 9, characterized in that: After performing a matching evaluation of the target photovoltaic system and the target cold storage system in each time period of the forecast day based on the matching result of each dimension, and obtaining the matching evaluation result of the target photovoltaic system and the target cold storage system in each time period of the forecast day, the method further includes: If the matching evaluation result between the target photovoltaic and the target cold storage in each time period of the forecast day is less than a preset threshold, the operation mode of the target cold storage is adjusted.

11. A time-space matching evaluation system for photovoltaic output and cold storage load, characterized in that: include: A photovoltaic output prediction module is used to determine the daily output of the target photovoltaic on the prediction day based on the solar illumination parameters of the target photovoltaic on the prediction day and the inherent photovoltaic parameters of the target photovoltaic; The cold storage load prediction module is used to consider the heat load of the cold storage enclosure structure, the heat load of the cold storage ventilation, the heat load of the cold storage cargo, the heat load of the cold storage operation and the heat load of the cold storage operation, and determine the total heat load of the target cold storage on the forecast day based on the cargo storage plan of the target cold storage on the forecast day, the weather conditions outside the target cold storage on the forecast day and the inherent heat-related parameters of the target cold storage; based on the thermoelectric conversion of the total heat load of the target cold storage on the forecast day, the total power load of the target cold storage on the forecast day is obtained; A matching evaluation module is used to perform multi-dimensional matching of the daily power output of the target photovoltaic power plant on the forecast day and the total power load of the target cold storage on the forecast day, and to perform matching evaluation of the target photovoltaic power plant and the target cold storage in each time period of the forecast day based on the matching results of each dimension, so as to obtain matching evaluation results of the target photovoltaic power plant and the target cold storage in each time period of the forecast day; Among them, the target cold storage is within the power supply range of the target photovoltaic.

12. The system according to claim 11, characterized in that The solar illumination parameters of the target photovoltaic on the predicted day include the relative position of the sun with respect to the target photovoltaic, the inherent photoelectric parameters of the target photovoltaic include the photovoltaic panel angle information of the target photovoltaic and the photoelectric conversion parameters of the target photovoltaic, and the photovoltaic output prediction module includes: A relative position calculation submodule, used to calculate the relative position information of the sun with respect to the target photovoltaic on the predicted day based on the solar optical parameters on the predicted day and the position information of the target photovoltaic; An incident angle calculation submodule, used to calculate the solar incident angle at the target photovoltaic on the forecast day based on the relative position information of the sun with respect to the target photovoltaic on the forecast day and the photovoltaic panel angle information of the target photovoltaic; The radiation calculation submodule is used to calculate the solar radiation received by the target photovoltaic on the forecast day based on the solar incident angle at the target photovoltaic on the forecast day, the solar optical parameters on the forecast day and the photovoltaic panel angle information of the target photovoltaic; The output prediction submodule is used to calculate the daily output of the target photovoltaic on the predicted day based on the solar radiation received by the target photovoltaic on the predicted day and the photoelectric conversion parameters of the target photovoltaic.

13. The system according to claim 12, characterized in that The solar optical parameters of the forecast day include the solar declination angle of the forecast day, the optical depth of the forecast day and the scattering factor of the forecast day. The relative position information of the sun relative to the target photovoltaic on the forecast day includes the solar altitude angle and the solar azimuth angle at the target photovoltaic on the forecast day. The calculation formula of the solar altitude angle at the target photovoltaic on the forecast day is: β=arcsin(cosL cosδcosH+sin L sinδ); Where, β is the solar altitude angle at the target photovoltaic on the forecast day, L is the geodetic latitude at the target photovoltaic, δ is the solar declination angle on the forecast day, and H is the solar hour angle at the target photovoltaic; The calculation formula for the solar azimuth angle at the target photovoltaic on the predicted day is: In the formula, φ S is the solar azimuth at the target photovoltaic on the predicted day; The photovoltaic panel angle information of the target photovoltaic includes the photovoltaic panel azimuth and photovoltaic panel tilt angle of the target photovoltaic. The calculation formula of the solar incident angle at the target photovoltaic on the predicted day is: α=arccos[cosβcos(φ S -f C )sinξ+sinβcosξ]; Where α is the solar incident angle at the target photovoltaic on the predicted day, φ C is the azimuth angle of the photovoltaic panel of the target photovoltaic, ξ is the tilt angle of the photovoltaic panel of the target photovoltaic; The calculation formula for the solar radiation received by the target photovoltaic on the predicted day is: I C =I BC +I DC +I RC ; I BC =I B cosα; In the formula, I C To predict the solar radiation received by the target photovoltaic system on a daily basis, I BC ,I DC ,I RC are the direct, scattered and reflected radiation that the photovoltaic panels of the target photovoltaic on the predicted day can receive, I B is the predicted direct sunlight intensity of the day, I SC is the solar constant of the prediction day, k is the optical depth of the prediction day, C is the scattering factor of the prediction day, ρ p is the reflectivity of the target photovoltaic site facing solar radiation; The target photovoltaic photoelectric conversion parameters include the target photovoltaic photovoltaic panel area and the target photovoltaic photovoltaic panel conversion efficiency. The calculation formula for the daily output of the target photovoltaic on the predicted day is: P PV =A p ×η×I C ; Where P PV To predict the daily output of the target photovoltaic power generation, A p is the photovoltaic panel area of ​​the target photovoltaic, and η is the photovoltaic panel conversion efficiency of the target photovoltaic.

14. The system according to claim 11, characterized in that The inherent heat-related parameters of the target cold storage include the enclosure structure parameters of the target cold storage, the enclosure structure heat transfer parameters and the air thermal parameters in the target cold storage; the cargo storage plan of the target cold storage includes the types of cargo stored in the target cold storage and the corresponding storage volume, ventilation conditions, lighting conditions, door opening and closing operation conditions, staff's in-store operation conditions and evaporator operation conditions; the cold storage load prediction module includes a cold storage heat load prediction submodule, and the cold storage heat load prediction submodule is used to: Based on the weather conditions outside the target cold storage on the forecast day, the enclosure structure parameters of the target cold storage and the enclosure structure heat transfer parameters of the target cold storage, the heat load of the enclosure structure of the target cold storage on the forecast day is calculated; Based on the ventilation conditions of the target cold storage on the forecast day, the air thermal parameters in the target cold storage on the forecast day, and the weather conditions outside the target cold storage on the forecast day, the ventilation heat load of the target cold storage on the forecast day is calculated; Based on the storage volume of various goods in the target cold storage on the forecast day and the changes in thermal parameters of various goods entering the target cold storage, the cargo heat load of the target cold storage on the forecast day is calculated; Based on the lighting conditions, door opening and closing operations and staff operations in the target cold storage on the forecast day, the operating heat load of the target cold storage on the forecast day is calculated; Determine the operating heat load of the target cold storage on the forecast day based on the operating condition of the evaporator of the target cold storage on the forecast day; Based on the enclosure heat load, ventilation heat load, cargo heat load, operation heat load and running heat load of the target cold storage on the forecast day, the total heat load of the target cold storage on the forecast day is determined.

15. The system according to claim 14, characterized in that The calculation formula for the total heat load of the target cold storage on the forecast day is as follows: Q T =Q w +Q v +Q p +Q m +Q e ; In the formula, Q T is the total heat load of the target cold storage on the predicted day, Q w To predict the heat load of the enclosure structure of the target cold storage on the day, Q v To predict the ventilation heat load of the target cold storage on a daily basis, Q p To predict the heat load of the cargo in the target cold storage, Q m To predict the operating heat load of the daily target cold storage, Q e To predict the operating heat load of the daily target cold storage; Q w =U×A c ×(T a -T is ) h0=5.62+3.9ν; Where U is the total heat transfer coefficient of the enclosure structure of the target cold storage, A c is the surface area of ​​the enclosure structure of the target cold storage, T a To predict the daily target cold storage external temperature, T is is the temperature inside the cold storage for the predicted day, h0 is the heat transfer coefficient of the air outside the cold storage for the predicted day, and h i is the predicted daily target cold storage internal surface air heat transfer coefficient, k w is the thermal conductivity of the wall of the enclosure structure of the target cold storage, Δω is the wall thickness of the enclosure structure of the target cold storage, and ν is the wind speed outside the target cold storage on the predicted day; Where n t To predict the daily ventilation times of the target cold storage, V n is the net volume of the target cold storage, Δh air is the enthalpy change of the air entering the target cold storage on the predicted day, ρ air is the air density in the target cold storage; In the formula, m j,p To predict the daily purchase volume of the jth type of goods, h j,1 、h j,2 are the specific enthalpy of the initial temperature when the j-th type of goods enters the target cold storage on the forecast day and the specific enthalpy when the temperature drops in the cold storage, q j,1 ,q j,2 are the respiration heat per unit mass of the jth cargo at the initial cooling temperature and the respiration heat per unit mass at the final cooling temperature on the forecast day, t j,1 ,t j,2 are the temperatures of the packaging materials corresponding to the jth type of goods on the forecast day when they enter the target cold storage and when the cooling is terminated, respectively. j,b 、c j,b are the mass and specific heat capacity of the outer packaging of the jth type of cargo on the forecast day, τ j is the cooling processing time of the jth type of goods; In the formula, Q d is the floor lighting heat flux per unit area of ​​the target cold storage, A d is the floor area of ​​the target cold storage, n k is the number of cold storage doors of the target cold storage, n k ' is the predicted target number of cold storage door openings per day, h w 、h n are the specific enthalpy of indoor and outdoor air in the target cold storage on the predicted day, M is the air curtain correction coefficient of the target cold storage, ρ n is the density of refrigerated goods in the target cold storage, N is the number of operators in the target cold storage on the forecast day, Q a is the average heat load released per person per unit time, t s Working hours for operators; Q e =N me S / m me ; Where N me is the number of evaporator fan motors of the target cold storage, S is the shaft power of the evaporator fan motor of the target cold storage, μ me Evaporator fan motor efficiency of the target cold storage.

16. The system according to claim 14, characterized in that The cold storage load prediction module also includes a thermoelectric conversion submodule, which is used to: Based on the condensation coefficient of the target cold storage on the forecast day obtained by fitting, the total heat load of the target cold storage on the forecast day is converted into electricity to obtain the power consumption of the compressor of the target cold storage on the forecast day; Based on the lighting power consumption, condenser motor power, evaporator motor power and compressor power consumption of the target cold storage on the forecast day, the total power load of the target cold storage on the forecast day is determined.

17. The system according to claim 11, characterized in that The matching evaluation module includes a matching submodule, and the matching submodule is used to: Based on the correlation analysis of the daily power output of the target photovoltaic system on the forecast day and the total power load of the target cold storage on the forecast day, the volatility matching index of each period of the forecast day in the volatility dimension is obtained; The power dimension is matched based on the ratio of the daily power output of the target photovoltaic system to the total power load at each forecast time on the forecast day, and the power matching index in the power dimension for each period of the forecast day is determined.

18. The system according to claim 17, characterized in that The matching evaluation module further includes an evaluation submodule, which is used to: Based on the membership function corresponding to the matching index of each dimension, the matching strength analysis is performed on the matching index of each dimension in each period of the forecast day to obtain the true value corresponding to the matching index of each dimension in each period of the forecast day; Based on the corresponding weight of each matching index on the forecast day, the real value corresponding to the matching index of each dimension in each time period of the forecast day is weighted and integrated to obtain the matching evaluation result between the target photovoltaic and the target cold storage in each time period of the forecast day; The weight corresponding to each matching index of the forecast day is determined based on the multi-dimensional matching results of several days adjacent to the forecast day.

19. The system according to claim 18, characterized in that The evaluation submodule is also used for: Obtain multi-dimensional matching results within the future target time range; Taking the daily multi-dimensional matching results within the future target time range as an evaluation object, taking the real value corresponding to the matching index of one dimension of each day as an evaluation index of the corresponding evaluation object, and based on the multi-dimensional matching results within the future target time range, using the spread-out method to determine the corresponding weight of each matching index on the forecast day; The future target time range includes the forecast date and several days adjacent to the forecast date.

20. The system according to any one of claims 11 to 19, characterized in that: Also includes: The operation control module is used to adjust the operation mode of the target cold storage if the matching evaluation result between the target photovoltaic and the target cold storage in each time period of the forecast day is less than a preset threshold.

21. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the spatiotemporal matching evaluation method for photovoltaic output and cold storage load according to any one of claims 1 to 10 is implemented.

22. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the time-space matching evaluation method for photovoltaic output and cold storage load as described in any one of claims 1 to 10 is implemented.

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