Regional distributed photovoltaic cluster division method considering multi-element association, equipment and medium

Abstract

The invention relates to a regional distributed photovoltaic cluster division method considering multi-element association, equipment and a medium. The method comprises the following steps: acquiring latitude and longitude information of a distributed photovoltaic power station in a current time region; calculating a spatial-temporal characteristic matrix of each distributed photovoltaic power station according to the time information and the latitude and longitude information; based on the spatial-temporal characteristic matrix of the distributed photovoltaic power stations, performing primary cluster division based on spatial-temporal characteristics on the distributed photovoltaic power stations in the region by adopting a clustering algorithm; based on the physical parameters of the photovoltaic power station, adopting a clustering algorithm to carry out secondary cluster division based on physical characteristics on the distributed photovoltaic power station under each sub-cluster obtained by the primary cluster division; and taking a sub-cluster classification condition after two-stage division as a final regional distributed photovoltaic cluster division result. Compared with the prior art, the method has the advantages of accurate and reliable cluster division and the like.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic data processing, and in particular to a method, device and medium for dividing regional distributed photovoltaic clusters considering the association of multiple factors. Background Art

[0002] The rapid development of distributed photovoltaics has played an important role in ensuring safe and reliable power supply, promoting green energy transformation and development, and driving farmers' income and employment, and has achieved good comprehensive social and economic benefits. At present, the randomness and uncertainty of distributed photovoltaic power generation output are relatively large. When distributed photovoltaics are connected to the grid on a large scale, the fluctuation of output will inevitably have an unignorable impact on the stable and safe power supply quality of the power network. Therefore, from the perspective of energy optimization and scheduling, the ultra-short-term prediction of photovoltaic power within the day can help the power grid and related energy management departments to better dispatch and manage the power system; from the perspective of safe and stable operation of the power system, photovoltaic power generation has a large volatility, and prediction can help grid operators predict the trend of photovoltaic power generation changes and peak-valley balance, thereby ensuring the stable operation of the power system.

[0003] At present, the installed capacity of distributed photovoltaic grid-connected is increasing year by year. How to cluster and manage distributed photovoltaic is an important means to help improve the accuracy of photovoltaic power prediction and the efficiency of power system management. Therefore, it is necessary to study the cluster division of distributed photovoltaic with different geographical locations and natural micro-meteorological environments.

[0004] At present, most of the research on photovoltaic systems in China is mainly focused on centralized photovoltaics, and most of the research on distributed photovoltaics is also focused on single photovoltaic power stations. Distributed photovoltaic cluster division research usually refers to the research on the effective division and centralized management of photovoltaic groups with different geographical distributions. Distributed photovoltaic systems usually refer to decentralized solar power generation systems that are smaller in scale than centralized photovoltaics. They are mostly arranged on the roofs of residential and commercial buildings or ground sites, scattered in different locations and regions. The purpose of cluster division research is to optimize the overall efficiency of photovoltaic systems, improve the efficiency of power management, and better integrate them into the power grid.

[0005] At present, one type of research on the access of distributed photovoltaic power station clusters to the distribution network is the study of simplified equivalent modeling. The cluster power stations are grouped according to the dynamic characteristics of the photovoltaic power station, and then the parameters of the power stations in the same group are aggregated and the network is simplified and equivalent to obtain the dynamic equivalent model of the photovoltaic cluster. Taking into account the meteorological environmental factors, and combining the grid search GridsearchCV to screen many factors, it is more suitable to divide the main classification indicators of distributed photovoltaic power stations into geographical parameters and irradiance as well as power and temperature, and divide these subclusters into 5 subclusters. However, the above-mentioned cluster photovoltaic sub-cluster division methods all need to be combined with the observation of meteorological environmental factors, but distributed photovoltaics are often scattered, and it is difficult to fully build a complete set of meteorological observation equipment, and the investment is also large.

[0006] Distributed photovoltaics are developing rapidly. Due to their scattered distribution over a wide area, how to reasonably and effectively divide regional large-scale distributed photovoltaic clusters, so as to obtain photovoltaic power station clusters with highly similar output characteristics, can help improve the output prediction accuracy of photovoltaic clusters. However, in the current cluster division process, the geographical location is simply divided using longitude and latitude, ignoring the influence of photovoltaic power station structural properties, astronomical geography and other factors that may affect the division of photovoltaic power station clusters. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method, device and medium for dividing regional distributed photovoltaic clusters taking into account the correlation of multiple factors.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to a first aspect of the present invention, a method for dividing a regional distributed photovoltaic cluster considering the association of multiple factors is provided, the method comprising:

[0010] Get the longitude and latitude information of the distributed photovoltaic power station in the current time area;

[0011] Calculate the spatiotemporal characteristic matrix of each distributed photovoltaic power station based on time information and longitude and latitude information;

[0012] Based on the spatiotemporal feature matrix of distributed photovoltaic power stations, a clustering algorithm is used to divide the distributed photovoltaic power stations in the region into clusters based on spatiotemporal features.

[0013] Based on the physical parameters of the photovoltaic power station, a clustering algorithm is used to perform secondary clustering based on physical characteristics on the distributed photovoltaic power stations under each sub-cluster obtained by the primary clustering.

[0014] The classification of sub-clusters after the two-level division is taken as the final regional distributed photovoltaic cluster division result.

[0015] Preferably, the parameters in the spatiotemporal characteristic matrix include the solar declination angle δ, the solar altitude angle α s , solar azimuth γ s and astronomical radiation G0.

[0016] Preferably, the sun altitude angle α s , the calculation expression is:

[0017] sinα s =sinφsinδ+cosφcosδcosω

[0018] Where: φ is the latitude of the PV panel installation location; ω is the solar hour angle, ω = 360° × (T-12) / 24 = 15(T-12), where T is the local apparent time.

[0019] Preferably, the solar azimuth angle γ s , the calculation expression is:

[0020]

[0021] Where: φ is the latitude of the PV panel installation location.

[0022] Preferably, the astronomical radiation G0 is calculated as:

[0023]

[0024] Where: S0 is the solar constant, ρ is the distance parameter between the sun and the earth.

[0025] Preferably, the clustering algorithm is a FCM clustering algorithm.

[0026] Preferably, in one cluster division, the classification parameter is set to 3 to 5 categories.

[0027] Preferably, the physical parameters of the photovoltaic power station include component materials and component installation methods.

[0028] According to a second aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, any one of the methods described above is implemented.

[0029] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, any one of the methods described above is implemented.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1) The present invention fully considers the multi-factor correlation information of time, space and matter, and adopts a primary clustering division based on time and space characteristics and a secondary clustering division based on physical characteristics for the distributed photovoltaic power station cluster, which can obtain a photovoltaic power station clustering result with high similarity, which is more accurate and reliable.

[0032] 2) The present invention constructs a spatiotemporal characteristic matrix based on the solar declination angle, solar altitude angle, solar azimuth angle and astronomical radiation. The solar altitude angle contains the spatiotemporal information of the photovoltaic power station due to the coupling of dimension, inclination angle and time angle. The selected astronomical radiation is more regular than the ground-observed radiation and is not affected by surface meteorological changes, so it can better reflect the spatiotemporal characteristic state of the distributed photovoltaic power station.

[0033] 3) The present invention sets the classification parameter N to 3-5 categories during a cluster division, which fully considers the requirements of power grid planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of the method of the present invention;

[0035] Figure 2 This is a schematic diagram of the distribution of a distributed photovoltaic power station in a certain place in the embodiment;

[0036] Figure 3 This is a schematic diagram of a cluster division result in the embodiment;

[0037] Figure 4 The effects of different physical characteristics on photovoltaic output under the same lighting conditions in the embodiment; wherein (a) and (b) correspond to different irradiance levels respectively;

[0038] Figure 5 Schematic diagram of the effect of different installation inclination angles on the output of a distributed photovoltaic power station under the same lighting conditions in the embodiment;

[0039] Figure 6 It is a schematic diagram of the secondary cluster division result in the embodiment. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] Example 1

[0042] This embodiment provides a method for dividing a regional distributed photovoltaic cluster considering the association of multiple factors, the method comprising:

[0043] Get the longitude and latitude information of the distributed photovoltaic power station in the current time area;

[0044] Calculate the spatiotemporal characteristic matrix of each distributed photovoltaic power station based on time information and longitude and latitude information;

[0045] Based on the spatiotemporal feature matrix of distributed photovoltaic power stations, a clustering algorithm is used to divide the distributed photovoltaic power stations in the region into clusters based on spatiotemporal features.

[0046] Based on the physical parameters of the photovoltaic power station, a clustering algorithm is used to perform secondary clustering based on physical characteristics on the distributed photovoltaic power stations under each sub-cluster obtained by the primary clustering.

[0047] The classification of sub-clusters after the two-level division is taken as the final regional distributed photovoltaic cluster division result.

[0048] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0049] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0050] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method in any other appropriate manner (e.g., by means of firmware).

[0051] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0052] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0053] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0054] Example 2

[0055] like Figure 1 As shown, this embodiment provides a method for dividing regional distributed photovoltaic clusters considering the association of multiple factors, and the method includes:

[0056] Step S1, obtaining the latitude and longitude information of the distributed photovoltaic power station in the area at the current time, and the default time data is the local time of the photovoltaic power station;

[0057] Figure 2 The diagram is a schematic diagram of the distribution of a distributed photovoltaic power station in a certain location selected in this embodiment.

[0058] Step S2: Calculate the spatiotemporal characteristic matrix of each distributed photovoltaic power station according to the longitude and latitude information; wherein the parameters in the spatiotemporal characteristic matrix include the solar declination angle δ, the solar altitude angle α s , solar azimuth γ s and astronomical radiation G0, the specific calculation expressions are:

[0059] The solar declination angle δ is calculated as:

[0060] δ=23.45°sin(2Π(N+284) / 365), δ∈[-23.45°, +23.45°]

[0061] Where: N represents the accumulated day, that is, the day of the year.

[0062] Sun altitude angle α s , the calculation expression is:

[0063]

[0064] Where: is the latitude of the PV panel installation location; ω is the solar hour angle, ω = 360° × (T-12) / 24 = 15(T-12), where T is the local apparent time.

[0065] Solar azimuth γ s , the calculation expression is:

[0066]

[0067] Where: The latitude of the PV panel installation location.

[0068] Astronomical radiation G0, the calculation expression is:

[0069]

[0070] Where: S0 is the solar constant; ρ is the distance parameter between the sun and the earth.

[0071] According to the above variable formula, the declination angle is coupled with the time quantity d, while the hour angle uses local time instead of Beijing time in the calculation process. Therefore, the hour angle contains the time period information and is also coupled with the longitude distribution of the power station (the division of time zones is based on longitude distribution). Therefore, due to the coupling of latitude, inclination and hour angle, the solar altitude angle contains the spatial characteristics and time information of the power station. The solar azimuth reflects the azimuth of the sun relative to the south at a certain moment. At the same reference time (Beijing time), the solar azimuths of different power stations are different. Compared with the solar radiation observed on the ground, astronomical radiation has no atmospheric attenuation, so it is often more regular than the total radiation observed on the ground. It is not affected by changes in surface meteorological conditions, so these variables reflect the spatiotemporal characteristic state of the power station.

[0072] Step S3: Based on the spatiotemporal feature matrix of the distributed photovoltaic power stations, a clustering algorithm is used to perform a cluster division of the distributed photovoltaic power stations in the region based on the spatiotemporal features;

[0073] Specifically, in the first cluster division, the classification parameters are set to 3 to 5 categories according to the grid planning requirements. The first cluster division results are shown in Figure 3 shown.

[0074] In this embodiment, the clustering algorithm is the FCM clustering algorithm, and other clustering algorithms may also be used.

[0075] Step S4: performing secondary clustering of the distributed photovoltaic power stations under each sub-cluster obtained by the primary clustering based on the physical parameters of the photovoltaic power station;

[0076] The physical parameters of a photovoltaic power station include photovoltaic module materials and module installation methods.

[0077] Since the power station data obtained in this embodiment are mostly installed in the same period and in the same installation method, the information of the power station materials obtained by physical characteristics is shown in Table 1. The influence of different physical characteristics on the photovoltaic output under the same lighting conditions is shown in Table 1. Figure 4 and Figure 5 , Figure 4 Parts (a) and (b) correspond to different irradiance levels.

[0078] Table 1 Photovoltaic module physical information and installation parameters

[0079] Site Number Component Information Installed capacity 1 Polysilicon 1,100kWp 2 Polysilicon 500kWp 3 Polysilicon 370kWp 4 Monocrystalline silicon 600kWp 5 Monocrystalline silicon 400kWp 6 Polysilicon 1,000kWp 7 Monocrystalline silicon 300kWp 8 Monocrystalline silicon 2,000kWp 9 Monocrystalline silicon 600kWp 10 Polysilicon 1,900kWp 11 Polysilicon 5,400kWp 12 Polysilicon 1,400kWp 13 Monocrystalline silicon 160kWp 14 Monocrystalline silicon 600kWp 15 Monocrystalline silicon 800kWp 16 Monocrystalline silicon 940kWp

[0080] Taking the physical characteristics of the power plant as input, the FCM clustering algorithm is used to perform secondary clustering. The results are shown in Figure 6 , the output correlation of different power stations in the sub-cluster is shown in Table 2-Table 6:

[0081] Table 2 Pearson correlation coefficient of power station output of cluster 1

[0082]

[0083]

[0084] Table 3 Pearson correlation coefficient of power station output of cluster 2

[0085] No.5 No.6 No.5 1 0.96878 No.6 0.96878 1

[0086] Table 4 Pearson correlation coefficient of cluster 3 power station output

[0087] No.7 No.8 No.9 No.7 1 0.957518 0.95216 No.8 0.96878 1 0.961326 No.9 0.95216 0.961326 1

[0088] Table 5 Pearson correlation coefficient of cluster 4 power station output

[0089] No.10 No.11 No.12 No.10 1 0.989778 0.990114 No.11 0.989778 1 0.986592 No.12 0.990114 0.986592 1

[0090] Table 6 Pearson correlation coefficient of cluster 5 power station output

[0091] No.13 No.14 No.15 No.16 No.13 1 0.973273 0.983587 0.954754 No.14 0.973273 1 0.972172 0.967172 No.15 0.983587 0.972172 1 0.956234 No.16 0.954754 0.967172 0.956234 1

[0092] It can be seen from Table 2-6 that the output of power stations in the same cluster has a high similarity.

[0093] Although only the characteristic of photovoltaic module materials is used in the specific implementation process in this embodiment, it does not limit the scope of application of this application. Specific physical characteristics can be appropriately added according to the difficulty of data acquisition in the actual application process.

[0094] Step S5: taking the classification of the sub-clusters after the two-level division as the final regional distributed photovoltaic cluster division result.

[0095] The other settings in this embodiment are the same as those in Embodiment 1.

[0096] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for dividing regional distributed photovoltaic clusters considering the association of multiple factors, characterized in that: The method includes: Get the longitude and latitude information of the distributed photovoltaic power station in the current time area; Calculate the spatiotemporal characteristic matrix of each distributed photovoltaic power station based on time information and longitude and latitude information; Based on the spatiotemporal feature matrix of distributed photovoltaic power stations, a clustering algorithm is used to divide the distributed photovoltaic power stations in the region into clusters based on spatiotemporal features. Based on the physical parameters of the photovoltaic power station, a clustering algorithm is used to perform secondary clustering based on physical characteristics on the distributed photovoltaic power stations under each sub-cluster obtained by the primary clustering. The classification of sub-clusters after the two-level division is taken as the final regional distributed photovoltaic cluster division result.

2. A method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 1, characterized in that: The parameters in the spatiotemporal characteristic matrix include the solar declination angle δ, the solar altitude angle α s , solar azimuth γ s and astronomical radiation G0.

3. A method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 2, characterized in that: The sun altitude angle α s , the calculation expression is: Where: is the latitude of the PV panel installation location; ω is the solar hour angle, ω = 360° × (T-12) / 24 = 15(T-12), where T is the local apparent time.

4. A method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 2, characterized in that: The solar azimuth angle γ s , the calculation expression is: Where: The latitude of the PV panel installation location.

5. A method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 2, characterized in that: The calculation expression of the astronomical radiation G0 is: Where: S0 is the solar constant, ρ is the distance parameter between the sun and the earth.

6. A method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 1, characterized in that: The clustering algorithm is the FCM clustering algorithm.

7. A method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 1, characterized in that: In a cluster division, the classification parameter is set to 3 to 5 categories.

8. The method for dividing regional distributed photovoltaic clusters considering multi-factor association according to claim 1, characterized in that: The physical parameters of the photovoltaic power station include component materials and component installation methods.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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